#### A Step-by-Step Guide to Become a Data Scientist

If you want to become a successful data scientist. But, you have no idea about the learning paths and sources to become a data scientist. Then, this article is for you wanna be a Data Scientist. But, I have a question before that.

## Do you really want to become a Data Scientist?

Let’s play a small quiz. Answer the below question in **YES **or **NO**.

If the answers to all are Yes then you can proceed further and start your journey to become a Data Scientist.

This quiz is not to frighten you, but to show you reality. You know data science is a buzzword nowadays, and many people start learning Data Science but quit in between. I don’t want you to quit. It’s just a reality check.

Questions:

- Do you love Mathematics, especially Linear algebra, Probability, and Statistics?
- Do you enjoy finding solutions to problems?
- Do you like to research and experiment?
- Do you enjoy coding?
- Do you have the patience to see results?
- Are you ready for sleepless nights?
- Are you a data lover?
- Are you ready to learn new things in different domains?
- Are you an imaginator and explorer?

If the answers to all the above questions are YES, then get ready to dive into the world of Data Science.

The journey of being a Data Scientist is not easy but interesting and satisfactory.

Let’s make this journey simple, enjoyable, and fruitful.

## What is a Data Scientist?

A scientist who works with data is called a Data Scientist. In this modern world of the internet, we are surrounded by huge data. According to research “The amount of data in the world was estimated to be 44 zettabytes at the dawn of 2020**.”** To understand zettabyte, one zettabyte has 21 zeroes.

Only knowing how much data is created every day means nothing if we can use it productively. Understanding data, analyzing data, to constructing all this data to useful information is the job of a **Data Scientist.**

Data Scientists are the one who solves complex data problems with their technical expertise in Maths, Statistics, and computer programming.

**Sound Interesting!!**

The work of a Data Scientist sounds so exciting, so exciting is the salary part of a Data Scientist. According to Glassdoor: “The national average salary for a Data Scientist is ₹9,97,034 in India. And “The national average salary for a Data Scientist is $113,309 in the United States.”

## Required skills to become a Data Scientist

Let’s discuss the skills required to become a Data Scientist. We will also see some free blogs, articles, and courses to learn these skills.

### 1. Programming language(Python or R)

First of all, we need a programming language to do a Data Scientist Job. Two languages are currently in trend Python and R.

Both Python and R are easy to learn and simple to implement. You just need some practice,

**Learn Python from the below sources:**

- Kaggle – Kaggle is a very good platform for Data Scientists and Machine learning Engineers. There are many tutorials available on Kaggle from which we can learn. There are two short courses for Python and Pandas(Python Library) on Kaggle. Kaggle provides you with some practice exercises as well.
- AnalyticsVidya – Another beautiful platform for Data Scientists is AnalyticsVidya. I have learned my first concept of Data Science through AnalyticsVidya. There is a free tutorial on Analytics Vidya to learn Python for Data Science. in this tutorial, you will get a Python setup tutorial as well.
- DataCamp – DataCamp also offers a free intro to Python course for data science. This tutorial contains pre-recorded videos for a better understanding of concepts. The most beautiful thing about data camp is that within the tutorial only you can get your hands dirty with coding.

**Learn R programming from the below sources:**

- AnalyticsVidya – AnalyticsVidya offers a beautiful free course for learning R for data science. AnalyticsVidya also collaborated with DataCamp for introductory tutorials on R. It also helps in setting up RStudio on your system.
- DataCamp – DataCamp offers two pre-recorded video courses for R programming for Data Science. The first is the Introduction to R course and The other is the intermediate R course.

### 2. Mathematics

Mathematics skills are a must to become a Data Scientist. Linear algebra, Matrics, Probability, and Statistics are areas Data Scientists need to be proficient in.

**Linear Algebra and Matrics**

Linear Algebra is a branch of mathematics concerned with linear equations represented through vectors and matrices.

- There are wonderful recorded video lectures by Professor Gilbert Strang of MIT available for Linear Algebra. This might take some time to complete these lectures but your understanding will be at an excellent level.
- You can free download The Matrix Algebra Cookbook by Petersen for detailed knowledge of Matrices.

**Probability and Statistics**

The probability helps make a prediction or the likelihood of an event. Statistics is largely dependent on the theory of probability. Both are the necessity of data scientists.

For detailed knowledge and hands-on on Probability and Statistics refer to a free e-book available by the name Think Stats: Probability and Statistics for Programmers. You can free download this book pdf.

*I want to mention here two important points:*

*I want to mention here two important points:*

*For mathematics topics, you can also refer to your 11th, 12th, graduation, and post-graduation Mathematics books. They will be easy to understand and handy all the time. Also, u can practice problems easily.*

- For interviews, the written test mathematics part is very important. Some companies even have more than 50% of questions based on Linear Algebra, Matrices, Probability, and Statistics. Although the percentage may vary from company to company.

### 3. Data Visualizations

Data Visualization is very helpful in understanding data. It can be called as detective work, to find out links between so huge data. Think as a common man, how data is more understandable- in tabular format or in Graphs and Plots format?

There are many types of graphs, plots, charts, and other visualization methods available in both Python and R programming, which can be learned easily and effectively from various sources like below:

- Kaggle – Kaggle is an amazing platform to learn and explore for Data Scientists and Machine learning Engineers. There is a mini-course available on Kaggle for Data visualization in Python.
- AnalyticsVidya – You can learn Data Visualizations in Python from Data Visualization Python blog of Analytics Vidya and for Data visualizations in R refer to data visualization r blog.
**DataCamp – DataCamp offered a course on Data Analysis in Python and Data Visualization in R in 3 parts.**- You can also download a free e-book on Exploratory Data Analysis in Python.

### 4. Machine Learning

Now, comes the major part of being a Data Scientist, Machine Learning knowledge. Below are the free courses available.

- Kaggle -Two courses by Kaggle on Machine Learning: Intro to Machine Learning and Intermediate Machine Learning. Here, you will get a chance to explore, learn, and implement ML models on the Titanic Dataset.
- Coursera – Machine Learning Course by
**Andrew Ng**is a superb recorded video course. Andrew Ng touches every concept of Machine Learning so smoothly. This will help you in creating your base of Machine Learning.

### 5. Feature Engineering

Feature engineering is very important to improve your model which helps in the rise in model performance. Some courses for feature engineering are:

- Short and crisp tutorial on Feature Engineering available on Kaggle.
- A blog on Analytics Vidya is very informative about feature engineering.

### 6. Natural Language Processing

Natural Language is a special field of Machine learning to process data in a natural language like English, Spanish, etc. It is to deal with text and voice data. In the internet age text data is huge and growing at a fast rate so is Natural Language Processing is growing at fast speed. Some good sources to learn Natural Language Processing are:

- Small Intro course of Natural Language Processing on Kaggle. But, I feel it is not sufficient.
- The ultimate article on Analytics Vidya for Natural Language Processing with Python Codes is a must.
- Natural Language Processing course on Coursera is very good and advanced. But, I feel its slightly difficult or a newbie.

### 7. Neural Networks and Deep Learning

Now, comes the advanced level of being a data scientist. This part is very crucial to understand. Deep Learning takes time and practice to build understanding. Advanced and user-friendly Python Libraries make your coding part easier. Some sources for Deep Learning are:

- Imagine learning from Stanford professors from the comfort of your home. It seems like a dream but possible now. The Deep Learning course by Stanford
**Professor Andrew Ng and****Kian Katanforoosh**is available now to learn. You can also watch classroom videos on YouTube of CS230: Deep Learning course. - Another option is the Deep Learning and Neural Network blog on Analytics Vidya. Here, is the link for the second part of this article.

### 8. Computer Vision(Convolutional Neural Network)

This is the most interesting part of the whole course. CNN is a special Neural Network that works like our eyes. It’s like giving human vision to the computer. On CNN we work with pictures and videos. Some interesting Sources to learn Computer Vision are:

- The first one is my favorite Course to learn Computer Vision from none other than Stanford Professor Andrej Karpathy and his team. A course on Convolution Neural Network from Stanford is now available to learn. Classroom-recorded videos are also available on YouTube. Go through it and learn.
- The blog on AnalyticsVidya on Convolutional Neural Network is amazing. This will get your base ready for the practical implementation of CNNs.

### 9. SQL

As Data Scientists we work with data and this data is stored in Databases. So, a data scientist needs to handle databases to work with data.

SQL is a Structured Query Language. It is an interface to access databases. Below are a few sources to learn SQL:

- There are two courses provided by Kaggle to learn SQL. The first is Intro to SQL and another one is Advanced SQL. Learn from these courses to write efficient queries.
- One another good source to learn SQL is two courses on Data Camp: Introduction to SQL and Intermediate SQL.
- The third is my old college-time favorite tutorial website i.e. w3schools. It is simple to understand and you can practice on the website itself.

These are the nine most required skills to become a successful Data Scientist.

Apart from the above sources, you can check for different topic-related courses on Coursera.org. There are many courses available on Coursera, but I have not gone through them, so I didn’t mention them in the above list. If you want to explore then go ahead!

The above courses and links are the best free learning sources I found to learn Data Science and Machine Learning. I have gone through much more stuff during my initial phase of learning Data Science basics, and then only did I come out with the best.

## Final Thoughts

I aim to save you time from unnecessary search for the best courses to learn Data Science. I hope this article will help you to be a successful Data Scientist.

In case you have any queries or you want to know more, please comment on the article. I will be glad to help aspiring data scientists.

Give me feedback if this article helps you in your Data Scientist journey.

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