Trending February 2024 # Why Python Alone Will Make You Fail In Data Science Job? # Suggested March 2024 # Top 8 Popular

You are reading the article Why Python Alone Will Make You Fail In Data Science Job? updated in February 2024 on the website Moimoishop.com. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested March 2024 Why Python Alone Will Make You Fail In Data Science Job?

Python though is a higher-level language with data centric libraries and easy to read syntax, it cannot perform the tasks efficiently at all the stages

A data science job requires programming knowledge. Data science mostly uses Python programming language. These are some ideas data science job seekers usually come across. Most of the opinions on the internet revolve around these ideas, which are only partial truths. Search for ‘most desirable data science skills’ only to find Python as one of the top skills required for data science. Indeed, Python, as a programming language has ruled the data science world ever since it was developed. This doesn’t mean learning Python alone would be sufficient to land a data science job. The reason might be on the part of the project’s requirement with respect to Python’s features or the aspirant’s programming capability – depending on python would be like putting all the eggs in one basket. Python, the popular language which is presumed to be indispensable for a data scientist is losing its ground to other programming languages. A data science project goes through different stages from data extraction to data modelling to model deployment. Python though is a higher-level language with data-centric libraries and easy-to-read syntax, it cannot perform the tasks efficiently at all the stages. The newcomers include SQL, R, Scala, Julia, etc with benefits like better Cloud Native performances and the ability to run on modern hardware, etc.

Python Vs Others – a comparison:

SQL comes into the picture when we look at how much and where the companies store data. For a successful database analysis, the data should be retrieved simultaneously from servers, which Python lags way behind when compared to the query language, SQL. No wonder SQL though holds equal importance appears trailing Python in the list of required skills. SQL is used for data retrieval which is an essential step for even getting started with the project. Employers look for people who are multitaskers within the data science domain adept at basic skills because most part of data science project involves gathering and cleaning data. Perhaps this is the reason why SQL has ranked higher than Python in the Stack overflow survey. SQL syntax comes in different formats which companies use according to the demands of the project. MySQL, and SQL Server, are a few of them, you need to give a try.

R was the most popular language for data science application in 2024-16 overtaken by Python in the last 2 to 3 years. R is more for seasoned pros for it is coded heavy and has a steep learning curve. Given the emerging trends which suggest machine learning moving away from data, there is very much chance that R might become the must-learn language for beginners. Whether to use R or Python shouldn’t be a question because the purpose or the data analysis goal differs. R is optimized for deep statistical analysis which data researchers employ for deep analytics and data visualisation features while Python is more suitable for data wrangling. When Burtch Works did a comprehensive survey of data scientists and analytics professionals, R was found more popular with experienced pros and Python with beginners.

Julia, an emerging language is still considered an add-on. It shares many features with Python, R, and other programming languages like Go and Ruby, it’s worth learning right in the beginning because it has the potential to replace Python for its superior performance. With Julia, it is possible to achieve C-like performance, and hand-crafted profiling techniques without optimization, which in Python’s case, is impossible. Why employ Python in the first place if Julia can make the job better?  Besides, Julia is good at working with external libraries, and memory management by default, and otherwise.

Looking beyond the Python paradigm

As said in the beginning, programming knowledge is not the be-all and end-all solution to securing a data science job. It is pretty much an obscure fact that employers look for problem solvers rather than number crunchers. Learning coding without paying attention to why you are doing it will take you nowhere. Learning data structures will not teach how to apply them to a given database for a particular problem. Well, there are many contenders like Scala and Swift which are fast making their way into the list of viable if not popular programming languages. To survive as a data scientist, better to let Python let be a necessity rather than a sufficient requirement.

More Trending Stories 

You're reading Why Python Alone Will Make You Fail In Data Science Job?

A/B Testing For Data Science Using Python – A Must

Overview

A/B testing is a popular way to test your products and is gaining steam in the data science field

Here, we’ll understand what A/B testing is and how you can leverage A/B testing in data science using Python

Introduction

Statistical analysis is our best tool for predicting outcomes we don’t know, using the information we know.

Picture this scenario – You have made certain changes to your website recently. Unfortunately, you have no way of knowing with full accuracy how the next 100,000 people who visit your website will behave. That is the information we cannot know today, and if we were to wait until those 100,000 people visited our site, it would be too late to optimize their experience.

This seems to be a classic Catch-22 situation!

This is where a data scientist can take control. A data scientist collects and studies the data available to help optimize the website for a better consumer experience. And for this, it is imperative to know how to use various statistical tools, especially the concept of A/B Testing.

A/B Testing is a widely used concept in most industries nowadays, and data scientists are at the forefront of implementing it. In this article, I will explain A/B testing in-depth and how a data scientist can leverage it to suggest changes in a product.

Table of contents:

What is A/B testing?

How does A/B testing work?

Statistical significance of the Test

Mistakes we must avoid while conducting the A/B test

When to use A/B test

What is A/B testing?

A/B testing is a basic randomized control experiment. It is a way to compare the two versions of a variable to find out which performs better in a controlled environment.

For instance, let’s say you own a company and want to increase the sales of your product. Here, either you can use random experiments, or you can apply scientific and statistical methods. A/B testing is one of the most prominent and widely used statistical tools.

In the above scenario, you may divide the products into two parts – A and B. Here A will remain unchanged while you make significant changes in B’s packaging. Now, on the basis of the response from customer groups who used A and B respectively, you try to decide which is performing better.

Source

It is a hypothetical testing methodology for making decisions that estimate population parameters based on sample statistics. The population refers to all the customers buying your product, while the sample refers to the number of customers that participated in the test.

How does A/B Testing Work?

The big question!

In this section, let’s understand through an example the logic and methodology behind the concept of A/B testing.

Let’s say there is an e-commerce company XYZ. It wants to make some changes in its newsletter format to increase the traffic on its website. It takes the original newsletter and marks it A and makes some changes in the language of A and calls it B. Both newsletters are otherwise the same in color, headlines, and format.

Objective

Our objective here is to check which newsletter brings higher traffic on the website i.e the conversion rate. We will use A/B testing and collect data to analyze which newsletter performs better.

1.  Make a Hypothesis

Before making a hypothesis, let’s first understand what is a hypothesis.

A hypothesis is a tentative insight into the natural world; a concept that is not yet verified but if true would explain certain facts or phenomena.

It is an educated guess about something in the world around you. It should be testable, either by experiment or observation. In our example, the hypothesis can be “By making changes in the language of the newsletter, we can get more traffic on the website”.

In hypothesis testing, we have to make two hypotheses i.e Null hypothesis and the alternative hypothesis. Let’s have a look at both.

Null hypothesis or H0:

The null hypothesis is the one that states that sample observations result purely from chance. From an A/B test perspective, the null hypothesis states that there is no difference between the control and variant groups. It states the default position to be tested or the situation as it is now, i.e. the status quo. Here our H0 is ” there is no difference in the conversion rate in customers receiving newsletter A and B”.

Alternative Hypothesis or H0:

The alternative hypothesis challenges the null hypothesis and is basically a hypothesis that the researcher believes to be true. The alternative hypothesis is what you might hope that your A/B test will prove to be true.

In our example, the Ha is- “the conversion rate of newsletter B is higher than those who receive newsletter A“.

Now, we have to collect enough evidence through our tests to reject the null hypothesis.

2. Create Control Group and Test Group

Once we are ready with our null and alternative hypothesis, the next step is to decide the group of customers that will participate in the test. Here we have two groups – The Control group, and the Test (variant) group.

The Control Group is the one that will receive newsletter A and the Test Group is the one that will receive newsletter B.

For this experiment, we randomly select 1000 customers – 500 each for our Control group and Test group.

Randomly selecting the sample from the population is called random sampling. It is a technique where each sample in a population has an equal chance of being chosen. Random sampling is important in hypothesis testing because it eliminates sampling bias, and it’s important to eliminate bias because you want the results of your A/B test to be representative of the entire population rather than the sample itself.

Another important aspect we must take care of is the Sample size. It is required that we determine the minimum sample size for our A/B test before conducting it so that we can eliminate under coverage bias. It is the bias from sampling too few observations.

3. Conduct the A/B Test and Collect the Data

One way to perform the test is to calculate daily conversion rates for both the treatment and the control groups. Since the conversion rate in a group on a certain day represents a single data point, the sample size is actually the number of days. Thus, we will be testing the difference between the mean of daily conversion rates in each group across the testing period.

When we run our experiment for one month, we noticed that the mean conversion rate for the Control group is 16% whereas that for the test Group is 19%.

Statistical significance of the Test

Now, the main question is – Can we conclude from here that the Test group is working better than the control group?

The answer to this is a simple No! For rejecting our null hypothesis we have to prove the Statistical significance of our test.

There are two types of errors that may occur in our hypothesis testing:

Type I error: We reject the null hypothesis when it is true. That is we accept the variant B when it is not performing better than A

Type II error: We failed to reject the null hypothesis when it is false. It means we conclude variant B is not good when it performs better than A

To avoid these errors we must calculate the statistical significance of our test.

An experiment is considered to be statistically significant when we have enough evidence to prove that the result we see in the sample also exists in the population.

That means the difference between your control version and the test version is not due to some error or random chance. To prove the statistical significance of our experiment we can use a two-sample T-test.

Source

To understand this, we must be familiar with a few terms:

Significance level (alpha):

The significance level, also denoted as alpha or α, is the probability of rejecting the null hypothesis when it is true. Generally, we use the significance value of 0.05

P-Value: It is the probability that the difference between the two values is just because of random chance. P-value is evidence against the null hypothesis. The smaller the p-value stronger the chances to reject the H0. For the significance level of 0.05, if the p-value is lesser than it hence we can reject the null hypothesis

Confidence interval: The confidence interval is an observed range in which a given percentage of test outcomes fall. We manually select our desired confidence level at the beginning of our test. Generally, we take a 95% confidence interval

Next, we can calculate our t statistics using the below formula:

Let’s Implement the Significance Test in Python

Let’s see a python implementation of the significance test. Here, we have a dummy data having an experiment result of an A/B testing for 30 days. Now we will run a two-sample t-test on the data using Python to ensure the statistical significance of chúng tôi can download the sample data here.

Python Code:



sns.distplot(data.Conversion_B)

At last, we will perform the t-test:

t_stat, p_val= ss.ttest_ind(data.Conversion_B,data.Conversion_A) t_stat , p_val (3.78736793091929, 0.000363796012828762)

For our example, the observed value i.e the mean of the test group is 0.19. The hypothesized value (Mean of the control group) is 0.16. On the calculation of the t-score, we get the t-score as .3787. and the p-value is 0.00036.

SO what does all this mean for our A/B Testing?

Here, our p-value is less than the significance level i.e 0.05. Hence, we can reject the null hypothesis. This means that in our A/B testing, newsletter B is performing better than newsletter A. So our recommendation would be to replace our current newsletter with B to bring more traffic on our website.

What Mistakes Should we Avoid While Conducting A/B Testing?

There are a few key mistakes I’ve seen data science professionals making. Let me clarify them for you here:

Invalid hypothesis: The whole experiment depends on one thing i.e the hypothesis. What should be changed? Why should it be changed, what the expected outcome is, and so on? If you start with the wrong hypothesis, the probability of the test succeeding, decreases

Testing too Many Elements Together: Industry experts caution against running too many tests at the same time. Testing too many elements together makes it difficult to pinpoint which element influenced the success or failure. Thus, prioritization of tests is indispensable for successful A/B testing

Ignoring Statistical Significance: It doesn’t matter what you feel about the test. Irrespective of everything, whether the test succeeds or fails, allow it to run through its entire course so that it reaches its statistical significance

Not considering the external factor: Tests should be run in comparable periods to produce meaningful results. For example, it is unfair to compare website traffic on the days when it gets the highest traffic to the days when it witnesses the lowest traffic because of external factors such as sale or holidays

When Should We Use A/B Testing?

A/B testing works best when testing incremental changes, such as UX changes, new features, ranking, and page load times. Here you may compare pre and post-modification results to decide whether the changes are working as desired or not.

A/B testing doesn’t work well when testing major changes, like new products, new branding, or completely new user experiences. In these cases, there may be effects that drive higher than normal engagement or emotional responses that may cause users to behave in a different manner.

End Notes

To summarize, A/B testing is at least a 100-year-old statistical methodology but in its current form, it comes in the 1990s. Now it has become more eminent with the online environment and availability for big data. It is easier for companies to conduct the test and utilize the results for better user experience and performance.

There are many tools available for conducting A/B testing but being a data scientist you must understand the factors working behind it. Also, you must be aware of the statistics in order to validate the test and prove it’s statistical significance.

To know more about hypothesis testing, I will suggest you read the following article:

Related

Launching The Data Science Immersive Bootcamp With Internship And Job Guarantee! (2024 Batch)

Introduction

Analytics Vidhya has been at the forefront of bridging the gap between theoretical knowledge and practical application of data science concepts for the past 7 years. With each passing year, we understood the problem better, and in December 2023, we launched the first edition of the Data Science Immersive Bootcamp.

And Analytics Vidhya is now thrilled to launch the 2nd Edition of Data Science Immersive Bootcamp.

Spanning over a duration of 6 months, the Bootcamp comes with-

500+Hours of Live online classes on Data Science, Data Engineering & Cloud Computing

500+Hours of Internship

20+Projects

12+Tools

50+Hours of Interview Preparation

But before we deep dive into further details of what we have to offer in the 2nd edition, let us highlight what we achieved in the first edition of Data Science Immersive Bootcamp.

Results of the First Bootcamp

The picture below is a testament to the fact that Analytics Vidhya’s Data Science Immersive Bootcamp created a dent in the universe with respect to how Data Science should be taught.

Companies that Participate in Analytics Vidhya’s Hiring Drives

With pan India reach, Analytics Vidhya has become one of the favorite platforms for recruiters in the AI and ML field. Giants like American Express and PaisaBazaar have been with us for a considerable time now. Here is a list of some of the prominent industry players who engage with us for recruiting-

Now let’s jump to see what we have to offer in the 2nd Edition of Data Science Immersive Bootcamp.

Unique Features of the Data Science Immersive Bootcamp (2nd Edition)

There are so many unique features that come with this Bootcamp which we bet you have not seen before anywhere. Here’s a quick summary of the highlights:

Data Science Immersive Bootcamp –  Internship

“In the Data Science Immersive Bootcamp, we are not only focusing on classroom training – we provide hands-on internship to enrich you with practical experience.”

This is precisely the motto of this program. To give you the best of both worlds- theory and industry application.

Here are some of the salient features of an internship-

Become industry-ready and learn on the job with a paid internship

Get a stipend of ₹10,000 per month

Get to work with the best data scientists from India’s leading Data Science community

Learn about the latest tools, frameworks, libraries and how to apply them to real-world projects

Let’s Gauge the Benefits of the Data Science Immersive Bootcamp

This Bootcamp has been created by keeping Data Science professionals at heart and industry requirements in mind. Let’s dive in to understand the benefits of Data Science Immersive Bootcamp:

Learn on the job from Day 1: This is a golden opportunity where you can learn data science and apply your learnings in various projects manned by you at Analytics Vidhya during the course of this Bootcamp

Work with experienced data scientists and experts: The best experts from different verticles will come together to teach and mentor you at the Bootcamp – it is bound to boost your experience and knowledge exponentially!

Work on real-world projects: Apply all that you learn on the go! Real challenges are faced when you dive in to solve a practical problem and cruising through that successfully will hone and fine-tune your blossoming data science portfolio

Peer Groups and Collaborative Learning: Best solutions are derived when you learn with the community! And this internship gives you an opportunity to be part of several focused teams working on different data projects

Build your data science profile: You will get to present your work in front of Analytics Vidhya’s thriving and burgeoning community with over 725,000 data science enthusiasts and practitioners. You are bound to shine like a star after getting such an exhaustive learning and hands-on experience

Mock interviews: Get the best hack to crack data science interviews

The Curriculum of Data Science Immersive Bootcamp

Data Science Immersive Bootcamp is one of the most holistic and intensive programs in the data science space. Consisting of 17 modules, here’s a high-level overview of what we will cover during this Bootcamp:

Module

1

Introduction

to Data

Science

and

understanding

the 

stages

of

Data

Science

lifecycle

Module

2

Problem

Formulation

and

Storytelling

using

Excel

Module

3

Build

Data

Stores

using SQL

Module

4

Build

Data

Stories

using

PowerBI

Module

5

Python

for

Data

Science

Module

6

Software

Engineering

Fundamentals

Module

7

Cloud

Computing

Module

8

– EDA

and

Statistics

Module

9

Getting

started

with

Machine

Learning

Module

10

Unsupervised

Machine

Learning

Models

Module

11

Feature

Engineering

and

Ensemble

Learning

Module

12

– Building Scalable

Models

Module

13

– Working

with

NoSQL

DataBases

Module

14 –

Time

Series

Forecasting

Module

15

Get

ting

started

with

Natural

Language

Processing

(

NLP

Module

16

Basics

of

Deep

Learning

Module

17

Model

Deployment

You can download the curriculum from here.

Wholesome Data Science is what we call it – everything you need to learn is presented in a single platter!

How to apply for the Data Science Immersive Bootcamp?

Here are the steps for the admission process to the Data Science Immersive Bootcamp:

Step 1- Apply

Apply for the Data Science Bootcamp program 2023 by filling up the registration form.

Step 2 – Assessments & Interviews

Take the Assessment tests, assignments and undergo the interview round.

Step 3 –

Join the Program

Get started on your journey to becoming an industry-ready data scientist

Here are the details of the program

Duration of Program: 6 months

Internship Stipend (from month 1): Rs. 10,000/- per month

Number of Projects: 20+ real-world projects

No. of Seats in the Bootcamp: Maximum 50

And The Most Awaited Aspect – You Get A Job Guarantee!

As mentioned above, this Bootcamp will enrich you with knowledge and industry experience thus making you the perfect fit for any role in Data Science. Bridging the gap between education and what employers want – the ultimate jackpot Analytics Vidhya is providing!

Build your data science profile and network

Create your own brand

Learn how to ace data science interviews

Craft the perfect data science resume

Work on real-world projects – a goldmine for recruiters

Harvard Business Review dubbed Data Scientist the sexiest job of the 21st Century.

And do not forget to register with us TODAY! Only 30 candidates will get a chance to unravel the best of Data Science in this specialized Bootcamp.

Related

Why Focusing On Seo Alone Isn’t Enough

“The only constant is change.”

As cliched as the phrase might be, it’s the perfect way to describe every marketer’s relationship with search engine optimization (SEO). Google’s algorithm is continually changing, which means that your SEO strategy is constantly evolving with it.

Luckily, Google’s webmaster guidelines clearly spell out what they’re looking for:

Make pages primarily for users, not for search engines.

Don’t deceive your users.

Avoid tricks intended to improve search engine rankings.

Think about what makes your website unique, valuable, or engaging. Make your website stand out from others in your field.

This post will explore why the most effective marketing teams aren’t putting all their eggs in one basket – because they know focusing on SEO alone isn’t enough.

SEOs Bring a Lot More to the Table Than Technical Skills

Before companies even begin to outline a digital marketing strategy, a lot of marketing teams are increasingly looking for well-rounded individuals who can combine an analytical understanding of SEO with the creativity required for engaging content (also known as a T-shaped marketer).

For example, Fractl (my employer) and Moz scraped more than 75,000 job listings and found that the volume of “SEO job” listings peaked between 2011 and 2012.

Only one job title containing “SEO” cracked the top five while more generalist positions such as Digital Marketing Manager and Marketing Manager were much more prevalent — indicating that SEO knowledge is a desirable skill when paired with many other marketing competencies.

These insights indicate that more brands are looking at their search marketing efforts through a different lens.

Although a technical SEO strategy is necessary, Google also places weight on the quality of your content.

Alt Attributes Don’t Answer Questions; High-Value Content Does

Keep in mind that the technical aspects of SEO — URL structure, headers, alt attributes, etc. — shouldn’t be overlooked. In fact, the above study also revealed that job titles containing “SEO” were averaging more than $100,000 annually.

Clearly, companies want people who know what they’re doing. However, relevant alt text isn’t what convinces someone to stay on your site; quality content does.

The “quality content” debate isn’t anything new to most marketers, but the latest SearchMetrics Report on ranking factors offers a new approach by coining the term “holistic content.” According to the report, holistic content incorporates relevant keywords that are similar to your target keywords in order to answer search intent more completely.

In other words, additional keywords are used to provide more comprehensive content, and if “you write a very good, readable text with lots of high-quality content,” not only will you generate more shares but your site will “rank equally well with search engines for many different keywords at the same time.”

Quality requires you to look beyond an extensive keyword list, though. An easy way to ensure your on-site content is up to Google’s high standards is by actually looking for what would signal low-quality content.

Below are red flags that are in your control and easily managed:

Broken links: Crawl your site and make sure there aren’t any “404 errors.”

Inaccurate information: Any sources you link to should be credible, whether they are internal or external links.

Page load speed: Having a fast-loading site can help you rank higher.

Comprehensiveness: Your content should answer all questions related to a specific topic.

Quality content also plays a big role in your ability to generate backlinks – an authoritative backlink profile is a key ingredient Google looks at when determining rankings. Sites will link to your content so long as it provides value, and below are three ways to ensure it does:

Your content offers something unique and original: You’re likely sitting on a ton of internal data that no one else has access to — share it!

There are actionable tips throughout: Usually someone lands on your content because it provides answers to a particular problem. Make sure they leave with insights they can use once they’re done reading.

It can stand alone as an evergreen resource: This is the holy grail for content — think laterally so that you answer every question possible, but also make sure it’s presented in a way that’s easy to digest.

The biggest thing to keep in mind is that your content should provide answers to real questions. This is what gets others to link to your content — and the more backlinks you have, the more value Google will add.

The Web Is Inherently Social, Which Makes Social Shares Valuable

While mining backlinks, you’ll notice a lot of social shares pop up — and they shouldn’t be overlooked, particularly when you think about how the web works.

People go online to share ideas, maintain relationships, and build new audiences. These are inherent social characteristics, which is why an effective SEO strategy places a lot of value on social media.

SEO alone can’t get your content in front of a large audience, but the increase in traffic that comes from highly shareable content is something Google will reward.

So what are some ways social media and SEO interact?

Social Media Profiles Typically Rank High on Branded Queries

Although social share counts don’t have a direct impact on your site’s ranking (according to Google), social profiles are typically some of the top results when people search for brand names.

For example, when I look up “Adidas” in Google, their Twitter and Facebook profiles rank 4th and 7th respectively, while the sport brand’s most active social channels are highlighted in the sidebar:

Social channels make the experience of getting to know your brand more fun and engaging, but they also let Google know you’re the real deal.

Social Networks Are Search Engines, Too

Facebook gets 2 billion searches per day. That means that a lot of people are using sites other than Google to get answers.

Brands should expand their concept of SEO to extend beyond traditional search engines.

Remember – social channels actually can serve as the initial source of information about your brand.

Amplify Your Content With Paid Social After the Initial Launch

Facebook, Twitter, and LinkedIn all have paid amplification options that can help you reach larger audiences, and considering more than 75% of B2C marketers are using paid social, it’s definitely a tactic worth investing in on your best content since these platforms tend to be a bit of an echo chamber.

Both SEO and social both help to build your brand identity to naturally attract visitors.

However, what social does that SEO can’t do is get your content in front of a much larger audience organically — which indirectly generates more backlinks and referral traffic that will help you rank higher.

URLs Draw Attention, but CTAs and Automation Drive Conversions

If you decided to focus solely on the more technical SEO tactics we’ve looked at up to this point, you could generate more traffic (albeit not as successfully). However, more visitors means nothing if people aren’t converting, which is why you can’t forget about how the sales funnel impacts your inbound strategy.

You can think about the sales funnel as three steps:

Be Seen. This is where all of the tactics we discussed earlier come into play. More than 80 percent of consumers use a basic online search to find out more about a brand, and an SEO strategy that combines a mix of on- and off-site content is crucial to optimize your search rankings.

Build Trust. This is where quality content kicks in. Providing valuable on-site content is how you distinguish yourself as a thought leader in your industry (and generate emails if you gate it).

SEO helps a lot with the first step, but engaging your audience is what pushes them further down the funnel.

One way to drive engagement is through CTAs, or calls to action. CTAs can range from entering an email address to making a purchase.

Another way to drive conversions is through automated email workflows.

Although blasting your subscriber list with the subject line “content marketing agency services” won’t help you rank any higher for that term, the email should include resources that your target audience will find useful — an essential element for any high-converting content.

There’s also a hidden bonus to email marketing.

A newsletter, for instance, can include widgets that allow readers to easily share content on their social channels. This can help drive the top-of-the-funnel awareness we looked at a bit earlier.

The regular email activity generated by automated workflows like newsletters is what encourages people to share your content with others. More shares means more people potentially seeing and engaging with your content, which improves the chances that your content will rank higher.

SEO Is Essential, But Ineffective in a Vacuum

Google’s algorithms are constantly changing. These changes are out of your control, which is why an effective marketing strategy shouldn’t focus solely on SEO.

You need to think of SEO as a companion to your social and content strategies.

Your ultimate goal should be to answer your audience’s most pressing questions through valuable content — and in return, they’ll reward you with more traffic.

Image Credits:

Top 10 Data Science Myths That You Should Ignore In 2023

Debunking the top 10 data science myths that you should ignore in the year 2023

In the world of Big Data, there are numerous job profiles available, such as Data Engineers, Data Analysts, Data Scientists, Business Analysts, and so on. Beginners need clarification on these profiles, as Data Scientist is the most popular and sought-after. They require assistance in determining whether Data Science is a good fit and identifying the best resources. There are several misconceptions about data science myths. As a data scientist, there are several data science myths to ignore for a successful career.

Transitioning into data science is difficult, not because you need to learn math, statistics, or programming. You must do so, but you must also combat the myths you hear from others and carve your path through them! In this article let us see the top 10 data science myths that you should ignore in 2023.

Myth 1 – Data Scientists Need to Be Pro-Coders

Your job as a Data Scientist would be to work extensively with data. Pro-coding entails working on the competitive programming end and having a strong understanding of data structures and algorithms. Excellent problem-solving abilities are required. Languages like Python and R in Data Science provide strong support for multiple libraries that can be used to solve complex data-related problems.

Myth 2 – Ph.D. or Master’s Degree is Necessary

This statement is only partly correct. It will be determined by the job role. A Master’s or Ph.D. is required if you want to work in research or as an applied scientist. However, if you want to solve complex data mysteries using Deep Learning/Machine Learning, you will need to use Data Science elements such as libraries and data analysis approaches. If you do not have a technical background, you can still enter the Data Science domain if you have the necessary skill sets.

Myth 3- All Data Roles are the Same

People believe that Data Analysts, Data Engineers, and Data Scientists all perform the same function. Their responsibilities, however, are completely different. The confusion arises because all of these roles fall under the Big Data umbrella. A Data Engineer’s role is to work on core parts of engineering and build scalable pipelines of data so that raw data from multiple sources can be pulled, transformed, and dumped into downstream systems.

Myth 4 – Data Science Is Only for Graduates of Technology

This is one of the most crucial myths. Many people in the Data Science domain come from non-tech backgrounds. Few people are transitioning from computer science to data science. Companies hire for data science and related positions, and many of those hired come from non-tech backgrounds with strong problem-solving abilities, aptitude, and understanding of business use cases.

Myth 5 – Data Science Requires a Background in Mathematics

As a Data Scientist, being good at math is essential, as data analysis requires mathematical concepts such as data aggregation, statistics, probability, and so on. However, these are not required to become a Data Scientist. We have some great programming languages in Data Science, such as Python and R, that provide support for libraries that we can use for mathematical computations. So, unless you need to innovate or create an algorithm, you don’t need to be a math expert.

Myth 6- Data Science Is All About Predictive Modelling

Data scientists spend 80% of their time cleaning and transforming data, and 20% of their time modeling data. There are numerous steps involved in developing a big data solution. The first step is data transformation. The raw data contains some error-prone values as well as garbage records. We need meaningful transformed data to build an accurate machine-learning model.

Myth 7- Learning Just a Tool Is Enough to Become a Data Scientist

The Data Science profile requires a diverse set of technical and non-technical skills. You must rely on something other than programming or any particular tool that you believe is used in Data Science. We need to interact with stakeholders and work directly with the business to get all of the requirements and understand the data domain as we work on complex data problems.

Myth 8- Companies Aren’t Hiring Freshers

This statement made sense a few years ago. However, today’s freshmen are self-aware and self-motivated. They are interested in learning more about data science and data engineering and are making efforts to do so. Freshers actively participate in competitions, hackathons, open-source contributions, and building projects, which aid in their acquisition of the necessary skill set for the Data Science profile, allowing companies to hire freshers.

Myth 9 – Data Science competitions will make you an expert

Data Science competitions are ideal for learning the necessary skills, gaining an understanding of the Data Science environment, and developing developer skills. However, competition will not help you become a Data Scientist. It will improve the value of your resume. However, to become an expert, you must work on real-world use cases or production-level applications. It is preferable to obtain internships.

Myth 10 – Transitioning cannot be possible in the Data Science domain

Don’t Miss Out On These 24 Amazing Python Libraries For Data Science

Overview

Check out our pick of the top 24 Python libraries for data science

We’ve divided these libraries into various data science functions, such as data collection, data cleaning, data exploration, modeling, among others

Any Python libraries you feel we should include? Let us know!

Introduction

I’m a massive fan of the Python language. It was the first programming language I learned for data science and it has been a constant companion ever since. Three things stand out about Python for me:

Its ease and flexibility

Industry-wide acceptance: It is far and away the most popular language for data science in the industry

The sheer number of Python libraries for data science

In fact, there are so many Python libraries out there that it can become overwhelming to keep abreast of what’s out there. That’s why I decided to take away that pain and compile this list of 24 awesome Python libraries covering the end-to-end data science lifecycle.

That’s right – I’ve categorized these libraries by their respective roles in data science. So I’ve mentioned libraries for data cleaning, data manipulation, visualization, building models and even model deployment (among others). This is quite a comprehensive list to get you started on your data science journey using Python.

Python libraries for different data science tasks:

Python Libraries for Data Collection

Beautiful Soup

Scrapy

Selenium

Python Libraries for Data Cleaning and Manipulation

Pandas

PyOD

NumPy

Spacy

Python Libraries for Data Visualization

Matplotlib

Seaborn

Bokeh

Python Libraries for Modeling

Scikit-learn

TensorFlow

PyTorch

Python Libraries for Model Interpretability

Lime

H2O

Python Libraries for Audio Processing

Librosa

Madmom

pyAudioAnalysis

Python Libraries for Image Processing

OpenCV-Python

Scikit-image

Pillow

Python Libraries for Database

Psycopg

SQLAlchemy

Python Libraries for Deployment

Flask

Python Libraries for Data Collection

Have you ever faced a situation where you just didn’t have enough data for a problem you wanted to solve? It’s an eternal issue in data science. That’s why learning how to extract and collect data is a very crucial skill for a data scientist. It opens up avenues that were not previously possible.

So here are three useful Python libraries for extracting and collection data.

One of the best ways of collecting data is by scraping websites (ethically and legally of course!). Doing it manually takes way too much manual effort and time. Beautiful Soup is your savior here.

Beautiful Soup is an HTML and XML parser which creates parse trees for parsed pages which is used to extract data from webpages.  This process of extracting data from web pages is called web scraping.

Use the following code to install BeautifulSoup:

pip install beautifulsoup4

Here’s a simple code to implement Beautiful Soup for extracting all the anchor tags from HTML:



I recommend going through the below article to learn how to use Beautiful Soup in Python:

Scrapy is another super useful Python library for web scraping. It is an open source and collaborative framework for extracting the data you require from websites. It is fast and simple to use.

Here’s the code to install Scrapy:

pip install scrapy

It is a framework for large scale web scraping. It gives you all the tools you need to efficiently extract data from websites, process them as you want, and store them in your preferred structure and format.

Here’s a simple code to implement Scrapy:

View the code on Gist.

Here’s the perfect tutorial to learn Scrapy and implement it in Python:

Selenium is a popular tool for automating browsers. It’s primarily used for testing in the industry but is also very handy for web scraping. Selenium is actually becoming quite popular in the IT field so I’m sure a lot of you would have at least heard about it.

We can easily program a Python script to automate a web browser using Selenium. It gives us the freedom we need to efficiently extract the data and store it in our preferred format for future use.

I wrote an article recently about scraping YouTube video data using Python and Selenium:

Python Libraries for Data Cleaning and Manipulation

Alright – so you’ve collected your data and are ready to dive in. Now it’s time to clean any messy data we might be faced with and learn how to manipulate it so our data is ready for modeling.

Here are four Python libraries that will help you do just that. Remember, we’ll be dealing with both structured (numerical) as well as text data (unstructured) in the real-world – and this list of libraries covers it all.

When it comes to data manipulation and analysis, nothing beats Pandas. It is the most popular Python library, period. Pandas is written in the Python language especially for manipulation and analysis tasks.

The name is derived from the term “panel data”, an econometrics term for datasets that include observations over multiple time periods for the same individuals. – Wikipedia

Pandas come pre-installed with Python or Anaconda but here’s the code in case required:

pip install pandas

Pandas provides features like:

Dataset joining and merging

Data Structure column deletion and insertion

Data filtration

Reshaping datasets

DataFrame objects to manipulate data, and much more!

Here is an article and an awesome cheatsheet to get your Pandas skills right up to scratch:

Struggling with detecting outliers? You’re not alone. It’s a common problem among aspiring (and even established) data scientists. How do you define outliers in the first place?

Don’t worry, the PyOD library is here to your rescue.

PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects. Outlier detection is basically identifying rare items or observations which are different significantly from the majority of data.

You can download pyOD by using the below code:

pip install pyod

How does PyOD work and how can you implement it on your own? Well, the below guide will answer all your PyOD questions:

NumPy, like Pandas, is an incredibly popular Python library. NumPy brings in functions to support large multi-dimensional arrays and matrices. It also brings in high-level mathematical functions to work with these arrays and matrices.

NumPy is an open-source library and has multiple contributors. It comes pre-installed with Anaconda and Python but here’s the code to install it in case you need it at some point:

$ pip install numpy

Below are some of the basic functions you can perform using NumPy:

Array creation

View the code on Gist.

output - [1 2 3] [0 1 2 3 4 5 6 7 8 9] Basic operations

View the code on Gist.

output - [1. 1.33333333 1.66666667 4. ] [ 1 4 9 36]

And a whole lot more!

We’ve discussed how to clean and manipulate numerical data so far. But what if you’re working on text data? The libraries we’ve seen so far might not cut it.

Step up, spaCy. It is a super useful and flexible Natural Language Processing (NLP) library and framework to clean text documents for model creation. SpaCy is fast as compared to other libraries which are used for similar tasks.

To install Spacy in Linux:

pip install -U spacy python -m spacy download en

To install it on other operating systems, go through this link.

Of course we have you covered for learning spaCy:

Python Libraries for Data Visualization

So what’s next? My favorite aspect of the entire data science pipeline – data visualization! This is where our hypotheses are checked, hidden insights are unearthed and patterns are found.

Here are three awesome Python libraries for data visualization.

Matplotlib is the most popular data visualization library in Python. It allows us to generate and build plots of all kinds. This is my go-to library for exploring data visually along with Seaborn (more of that later).

You can install matplotlib through the following code:

$ pip install matplotlib

Below are a few examples of different kind of plots we can build using matplotlib:

Histogram 3D Graph

Since we’ve covered Pandas, NumPy and now matplotlib, check out the below tutorial meshing all these three Python libraries:

Seaborn is another plotting library based on matplotlib. It is a python library that provides high level interface for drawing attractive graphs. What matplotlib can do, Seaborn just does it in a more visually appealing manner.

Some of the features of Seaborn are:

A dataset-oriented API for examining

relationships

between

multiple variables

Convenient views onto the overall

structure

of complex datasets

Tools for choosing

color palettes

that reveal patterns in your data

You can install Seaborn using just one line of code:

pip install seaborn

Let’s go through some cool graphs to see what seaborn can do:

View the code on Gist.

Here’s another example:

View the code on Gist.

Got to love Seaborn!

Bokeh is an interactive visualization library that targets modern web browsers for presentation. It provides elegant construction of versatile graphics for a large number of datasets.

Bokeh can be used to create interactive plots, dashboards and data applications. You’ll be pretty familiar with the installation process by now:

pip install bokeh

Feel free to go through the following article to learn more about Bokeh and see it in action:

Python Libraries for Modeling

And we’ve arrived at the most anticipated section of this article – building models! That’s the reason most of us got into data science in the first place, isn’t it?

Let’s explore model building through these three Python libraries.

Like Pandas for data manipulation and matplotlib for visualization, scikit-learn is the Python leader for building models. There is just nothing else that compares to it.

In fact, scikit-learn is built on NumPy, SciPy and matplotlib. It is open source and accessible to everyone and reusable in various contexts.

Here’s how you can install it:

pip install scikit-learn

Scikit-learn supports different operations that are performed in machine learning like classification, regression, clustering, model selection, etc. You name it – and scikit-learn has a module for that.

I’d also recommend going through the following link to learn more about scikit-learn:

Developed by Google, TensorFlow is a popular deep learning library that helps you build and train different models. It is an open source end-to-end platform. TensorFlow provides easy model building, robust machine learning production, and powerful experimentation tools and libraries.

An entire ecosystem to help you solve challenging, real-world problems with Machine Learning – Google

TensorFlow provides multiple levels of abstraction for you to choose from according to your need. It is used for building and training models by using the high-level Keras API, which makes getting started with TensorFlow and machine learning easy.

Go through this link to see the installation processes. And get started with TensorFlow using these articles:

What is PyTorch? Well, it’s a Python-based scientific computing package that can be used as:

A replacement for NumPy to use the power of GPUs

A deep learning research platform that provides maximum flexibility and speed

Go here to check out the installation process for different operating systems.

PyTorch offers the below features:

Hybrid Front-End

Tools and libraries: An active community of researchers and developers have built a rich ecosystem of tools and libraries for extending PyTorch and supporting development in areas from computer vision to reinforcement learning

Cloud support: PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling through prebuilt images, large scale training on GPUs, ability to run models in a production scale environment, and more

Here are two incredibly detailed and simple-to-understand articles on PyTorch:

Python Libraries for Data Interpretability

Do you truly understand how your model is working? Can you explain why your model came up with the results that it did? These are questions every data scientist should be able to answer. Building a black box model is of no use in the industry.

So, I’ve mentioned two Python libraries that will help you interpret your model’s performance.

LIME is an algorithm (and library) that can explain the predictions of any classifier or regressor. How does LIME do this? By approximating it locally with an interpretable model. Inspired from the paper “Why Should I Trust You?”: Explaining the Predictions of Any Classifier”, this model interpreter can be used to generate explanations of any classification algorithm.

Installing LIME is this easy:

pip install lime

This article will help build an intuition behind LIME and model interpretability in general:

I’m sure a lot of you will have heard of chúng tôi They are market leaders in automated machine learning. But did you know they also have a model interpretability library in Python?

H2O’s driverless AI offers simple data visualization techniques for representing high-degree feature interactions and nonlinear model behavior. It provides Machine Learning Interpretability (MLI) through visualizations that clarify modeling results and the effect of features in a model.

Go through the following link to read more about H2O’s Driverless AI perform MLI.

Python Libraries for Audio Processing

Audio processing or audio analysis refers to the extraction of information and meaning from audio signals for analysis or classification or any other task. It’s becoming a popular function in deep learning so keep an ear out for that.

LibROSA is a Python library for music and audio analysis. It provides the building blocks necessary to create music information retrieval systems.

Here’s an in-depth article on audio processing and how it works:

The name might sound funny, but Madmom is a pretty nifty audio data analysis Python library. It is an audio signal processing library written in Python with a strong focus on music information retrieval (MIR) tasks.

You need the following prerequisites to install Madmom:

NumPy

SciPy

Cython

Mido

And you need the below packages to test the installation:

PyTest

PyAudio

PyFftw

The code to install Madmom:

pip install madmom

We even have an article to learn how Madmom works for music information retrieval:

pyAudioAnalysis is a Python library for audio feature extraction, classification, and segmentation. It covers a wide range of audio analysis tasks, such as:

Classify unknown sounds

Detect audio events and exclude silence periods from long recordings

Perform supervised and unsupervised segmentation

You can install it by using the following code:

pip install pyAudioAnalysis

Python Libraries for Image Processing

So make sure you’re comfortable with at least one of the below three Python libraries.

When it comes to image processing, OpenCV is the first name that comes to my mind. OpenCV-Python is the Python API for image processing, combining the best qualities of the OpenCV C++ API and the Python language.

It is mainly designed to solve computer vision problems.

OpenCV-Python makes use of NumPy, which we’ve seen above. All the OpenCV array structures are converted to and from NumPy arrays. This also makes it easier to integrate with other libraries that use NumPy such as SciPy and Matplotlib.

Install OpenCV-Python in your system:

pip3 install opencv-python

Here are two popular tutorials on how to use OpenCV in Python:

Another python dependency for image processing is Scikit-image. It is a collection of algorithms for performing multiple and diverse image processing tasks.

You can use it to perform image segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and much more.

We need to have the below packages before installing scikit-image:

And this is how you can install scikit-image on your machine:

pip

install

-

U

scikit

-

learn

Pillow is the newer version of PIL (Python Imaging Library). It is forked from PIL and has been adopted as a replacement for the original PIL in some Linux distributions like Ubuntu.

Pillow offers several standard procedures for performing image manipulation:

Per-pixel manipulations

Masking and transparency handling

Image filtering, such as blurring, contouring, smoothing, or edge finding

Image enhancing, such as sharpening, adjusting brightness, contrast or color

Adding text to images, and much more!

How to install Pillow? It’s this simple:

pip install Pillow

Check out the following AI comic illustrating the use of Pillow in computer vision:

Python Libraries for Database

Learning how to store, access and retrieve data from a database is a must-have skill for any data scientist. You simply cannot escape from this aspect of the role. Building models is great but how would you do that without first retrieving the data?

I’ve picked out two Python libraries related to SQL that you might find useful.

The current psycopg2 implementation supports:

Python version 2.7

Python 3 versions from 3.4 to 3.7

PostgreSQL server versions from 7.4 to 11

PostgreSQL client library version from 9.1

Here’s how you can install psycopg2:

pip install psycopg2

Ah, SQL. The most popular database language. SQLAlchemy, a Python SQL toolkit and Object Relational Mapper, gives application developers the full power and flexibility of SQL.

It is designed for efficient and high-performing database access. SQLAlchemy considers the database to be a relational algebra engine, not just a collection of tables.

To install SQLAlchemy, you can use the following line of code:

pip install SQLAlchemy

Python Libraries for Deployment

Do you know what model deployment is? If not, you should learn this ASAP. Deploying a model means putting your final model into the final application (or the production environment as it’s technically called).

Flask is a web framework written in Python that is popularly used for deploying data science models. Flask has two components:

Werkzeug: It is a utility library for the Python programming language

Jinja: It is a template engine for Python

Check out the example below to print “Hello world”:

View the code on Gist.

The below article is a good starting point to learn Flask:

End Notes

In this article, we saw a huge bundle of python libraries that are commonly used while doing a data science project. There are a LOT more libraries that are out there but these are the core ones every data scientist should know.

Related

Update the detailed information about Why Python Alone Will Make You Fail In Data Science Job? on the Moimoishop.com website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!