# What Skills Do You Need to Become a Data Scientist?

If you want to be a data scientist, you will need to learn about a wide range of topics and several skills. Although top universities offer courses teaching these topics, taking those courses could cost you thousands of dollars and months of effort. So naturally, most of us want to learn data science on our own. Ideally, you could learn in your free time, without giving up your other commitments.

You can learn the skills needed to become a data scientist. The truth is that a data scientist never stops learning — I know I don’t.

Let me show you how.

Last Updated March 2021

Machine Learning A-Z, Data Science, Python for Machine Learning, Math for Machine Learning, Statistics for Data Science | By Jitesh Khurkhuriya, Python, Data Science & Machine Learning A-Z Team

Explore Course**Why most aspiring data scientists give up**

If you’ve already attempted to teach yourself data science and given up, don’t panic. The problem with most resources that claim to teach you data science is that they don’t provide a clear learning path. To become a data scientist, you don’t need to master every skill. You just need to learn the basics of 5 disciplines:

**Mathematics****Statistics****Python****Data visualization****Machine learning**

Let’s dive deep into these and find out what you need to learn and what you don’t.

** Mathematics: Why math?**

Let’s start with mathematics. Why do you need to understand math? You need math to communicate with a computer. Mathematical terms express most algorithms in machine learning.

**What areas of math should a data scientist learn?**

- A beginner should start with the rate of change and limits of a function
- Differential calculus or derivatives
- Derivatives and partial derivatives to understand how gradient descent or optimization works
- Linear algebra, specifically:
- Vectors
- Unit vectors
- Matrices
- Performing arithmetic operations on vectors and matrices
- Vector dot product
- Vector transformation
- Change of Basis
- Eigenvectors and eigenvalues.

- Basic probability terms like:
- Conditional probability
- Random processes
- Random variables

This may seem like a lot. Remember these are the key skills from much larger areas. You don’t need to learn all of linear algebra, for example, just a few selections from it.

A good data scientist must be familiar with the basic concepts of some math. These include exponents, logarithms, polynomial equations, factoring, quadratic equations, and functions. You don’t need to practice these too much or go in-depth at this stage. Just make sure you understand the concepts and know them a little. The concepts of differential calculus or derivatives are a must for any data scientist.

Almost all machine learning algorithms work on the principle of optimization. This includes neural networks. The gradient descent achieves this optimization. That means if you want to work with the popular area of neural networks, you’ll need to understand that optimization.

If you want to do predictive analytics, you’ll need to understand linear algebra. It forms the backbone of the predictive analytics of data science and machine learning. Linear algebra will help you understand how various algorithms work.

Classification problems all use probability. Probability can help you make predictions like, “Will this customer buy?” and “Will this customer default on the loan?” Inferential statistics create probability distributions to draw inferences about the data. It’s important to understand common probability terms.

**Statistics: Why statistics?**

Now that we’ve covered math skills, it’s time to talk about statistics. There are specific areas of statistics that are important for data science. You will need to know these to look at and understand the data of data science.

**What statistics concepts should a data scientist learn?**

- Descriptive statistics
- Correlations
- Inferential statistics. The key concepts are:
- Probability distributions
- Bell curve or normal distribution
- Central limit theorem
- Confidence interval
- Hypothesis testing

Descriptive statistics help us get some simple but very crucial aspects of data. Descriptive statistics are a straightforward interpretation of the data. They use the central tendency of the data. We identify one central value where most of the data is. We also identify the measure of dispersion, which helps us understand the data’s spread.

If you want to understand different machine learning algorithms, you will need to be able to look at correlations. Various correlations among the data points are useful in data understanding and data selection.

For real data, you’ll often need inferential statistics. Inferential statistics help us draw inferences or conclusions about the real data based on samples. Inferential statistics create probability distributions to draw inferences about the data.

**Python: Why Python?**

Python is a programming language that many data scientists use over others. This is because Python has various modules and functions needed for data science. Remember, you don’t need to be an expert in Python programming. You just need to know what you’ll use for data science and machine learning. Then you need to practice them across different scenarios.

**What should a data scientist know in Python?**

- File processing
- Data types
- Loops
- Various math functions

File processing is one of the most critical concepts in Python that every data scientist should know. You need to focus on basic read, append, and writing of the file. Also, one should know how to apply loops on the read data.

You’ll also need to get a basic understanding of all the data types. In particular, data science uses the string, numeric, and list types of variables. Once you know these, you will need to master loops with list and string variables.

You should focus on learning various math functions within Python. You will also need date modules and string functions. The most important ones for data science are the length, slicing and indexing, split, and strip. You will also need to know list functions and methods for search, length, and how to handle a multidimensional list.

**Data visualization: Why data visualization?**

They say a picture is worth a thousand words. Visualization gives that picture. A simple visualization of the data can help us draw inferences or identify data patterns.

**What does a data scientist need to know for data visualization?**

- A library like Matplotlib to create plots
- Basic plots like:
- Histograms
- Bar charts
- Line plots
- Scatter plots
- Boxplots

- Chart creation and customization
- Qualitative features like frequency charts, histograms, and pie charts

You can focus on building your data visualization skills using a library. One excellent package for data visualization for Python is Matplotlib. Matplotlib has many features and readymade functions. Try to create plots using simple data using Python lists before moving on to the complex real-world data from large files.

As a data scientist, you also need to know the basics of chart customization. Focus on drawing the charts with default parameters first and then progress to customization. A quick understanding and creation of templates can help you visualize the data in almost all future projects. You may need to use chart customizations as part of your presentation to various stakeholders. You’d have to explain the data patterns that you observed using different charts. In that case, you can create multiple plots in one attempt. You will want to make these charts visually appealing. That’s where various chart customization tools like figures and subplots become essential. At that point, you will also need to edit multiple chart elements like markers and line properties.

You’ll also need graphic visualization of qualitative data. Qualitative data can be words instead of numbers. This can be information like car color, gender, and marital status. Graphical visualization of qualitative data can help. It can identify similarities or various relationships among data elements.

**Machine learning: Why machine learning?**

A data scientist spends 70% of their time in data manipulation and data processing. You analyze data using various tools and machine learning algorithms for predictions. Before you can do so, you need to clean the data, explore, and process it. It is incredibly efficient, easy to learn, and very versatile in processing all kinds of data.

**What tools do you need for machine learning?**

- Pandas
- Regression
- Multiple linear regression
- Polynomial regression

- Classification methods:
- Logistic regression
- Decision trees
- Support vector machines

- Feature selection

Every data scientist should learn Pandas. It is important to read the data from different types of sources. You may have to convert various texts to numerical data. You may also need to split the data into train and test categories. That means you also need to learn modules for splitting the data into train and test.

Regression helps data scientists understand the relationship between different types of variables. You can use regression to predict numerical values. These can be things like the future price of a stock or next quarter’s sales. When you do this you can re-use data processing templates to save time and focus more on the core concepts.

Regression is one of the first models everyone learns during their machine learning journey. Focus on multiple linear regression and polynomial regression from the scikit-learn library. Learn and practice the effect of various parameters of these modules and functions.

Like regression analysis, classification methods are popular for predicting categorical outcomes. These can be things like, “Will this customer buy my product?” or “Will this customer default on the loan repayments?” Learning logistic regression, decision trees, and support vector machines is important for solving classification problems. Learn these 3 as well as what each of the parameters for these algorithms does. While there are other types of methods, remember, our focus here is not to learn every algorithm.

Feature selection separates the professionals from the amateurs. You can include almost all or some of the features for predictions based on common sense. It’s impossible to understand each variable when dealing with hundreds of features. That’s why we need to use various statistical analyses. We need to select features that have the greatest prediction capabilities. Focus on reading the data from different types of files, creating data frames, and reading from the data frame. You should also focus on various popular methods and functions like shape, index, columns, sum, describe, sort_values, and loc.

**Don’t forget to track your progress **

It is important to track your progress as you learn these skills. You can create a plan to track your progress weekly or daily. You can learn in detail about creating a tracker or planner.

While studying these topics, you should also focus on showcasing your knowledge and achievements to the world. These days your public profile can make a huge difference in hiring decisions. Your resume alone is not going to cut it. You’ll need to learn how to make your own data science project portfolio.

**The journey to becoming a data scientist starts now**

This article highlights which skills and which subjects are essential to learn, and at what level. You do not need to understand every skill and subject in detail although some skills will require in-depth learning. These are the key skills to become a successful data scientist. My course on Udemy addresses all these skills and can help you get on the journey to become a data scientist. Learn about why and how to teach yourself data science in** this blog article.**

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