DeZyre career counsellors often come across various questions from data science beginners on pre-requisites for learning data science. Of the many, some of the most common questions are –

*“How do I learn statistics for data science?”*

*“What are the topics/courses in statistics that I need to learn for excelling at data science?”*

*“What statistics concepts should I know for doing data science?”*

The objective of this blog post is to answer all the above questions and provide data science beginners with a structured path that will help them learn required statistics concepts used for data science and machine learning. Probability and Statistics are the foundation pillars for learning data science and machine learning as most of the data scientists come from one of those related areas like Economics, Computer Science, Applied Mathematics or Statistics.

According to William Chen, a data scientist at Quora – “*For any aspiring data scientist, I would highly recommend learning statistics with a heavy focus on coding up examples, preferably in Python or R.”*

*If you would like more information about Data Science Training, click the Request Info. button on top of this page.*

The most important probability and statistical concepts required to learn data science include –

- Descriptive Statistics, Distributions, Regression and Hypothesis Testing – The job role of a data scientist involves making meaningful decisions on a daily basis which could vary from making major decisions like designing the team’s R&D strategy or can be small business decision on how to tune a machine learning model. All this decision making process requires data scientists to have a strong foundation in core statistics concepts.
- Bayesian Thinking Concepts – Conditional Probability, Posteriors, Priors, and Maximum Likelihood –Bayesian Thinking in statistics involves using probability to model sampling processes and measure uncertainty if any before data collection. The level of uncertainty before data collection is often referred to as prior probability and after data collection is referred to as posterior probability. These are major concepts for developing most of the machine learning models and hence it is important to master them.
- Introduction to Statistics for Machine Learning – Learn basic machine learning concepts to understand how statistics fits in. Machine Learning and Statistics are closely related disciplines and to master modern machine learning it is necessary to understand the statistical machine learning approach.

There are many free online statistics courses and resources that can help data science beginners learn the core concepts of statistics needed for doing data science. These statistics courses online will help data science beginners learn the underlying theoretical concepts upfront without having to read a complete book. You do not need a math or statistics degree to succeed as a data scientist but by taking up the list of free online statistics courses you can have an added advantage over other aspiring data scientists as these statistic courses online will equip you with all the basic concepts of statistical thinking needed for doing data science.

DeZyre picks for statistics course online for budding data scientists are listed below -

A perfect course to master the concepts of descriptive statistics before learning data science w. The Statistics 2.1x course is an excellent guide for data science beginners that will familiarize them with various statistical terms and their definitions. This statistics course will also help you master other statistical concepts like variability, standard normal distribution, sampling distribution and central tendency. Anybody can take up this online statistics course for free as it does not have any pre-requisites or requires any prior knowledge of statistics.

Having mastered the concepts of Descriptive Statistics, it is necessary to learn the essential inferential statistics concepts like estimation, hypothesis testing, t-tests, ANOVA, Correlation and Regression. This free online statistics course on descriptive statistics spans for approximately 8 weeks and requires basic knowledge of central limit theorem, normal and sampling distributions, probability distributions and mean, mode and median concepts.

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An ideal statistics online course spanning for 4 weeks created by the University of California for people learning how to do analysis and also for decision makers. Learners taking up this statistics course should have already completed the Introduction to Statistics course and must have basic knowledge of Calculus concepts. An intermediate level statistics course for data science beginners to master various Bayesian Statistics concepts like Probability Distribution, Conditional Probability, Bayes Theorem, Priors and Models for Discrete Data and Continuous Data.

The most basic statistics course created by the University of Edinburgh that lets learners explore various ideas and methods behind the day to day statistics. Having basic secondary school mathematics knowledge is enough to take up this statistics course online. This intro to statistics online course spanning up to 6 weeks covers the basic definition on “What is Statistics?” and goes on to explaining the important methods of data collection, identifying data patterns, interpreting relationships, understanding uncertainty in data and statistical testing procedures.

Having completed the Inferential Statistics Course by Udacity, data science beginners should take up the statistical inference course to understand the far-reaching directions of inferential statistics which will help them make informed choices while doing data science.

Get started now to learn statistical concepts with these free online statistics courses for data science.

Apart from taking up these online statistics classes on introduction to statistics concepts, there are couple of good books to learn statistics for data science using either Python or R programming language –

- For data science beginners who want to learn statistics focussed on Python data science programming language, Think Stats is a must read.
- For data science beginners who want to learn statistics focussed on R data science programming language, The Elements of Statistical Learning and An Introduction to Statistical Learning are a must read.

We hope that this list of free statistics classes or online statistics courses will be of good use for data science beginners before enrolling for any comprehensive certified data science training. For professionals who have already taken either of these statistics online courses, share you experience or feedback in the comments below.

With outbreak of layoff announcements being made in the IT sector, up-skilling oneself with the latest in-demand technological skills like big data, data science , machine learning, artificial intelligence, internet of things and business analytics can make an IT professional indispensable to the organizations. Latest technological skills like data science and machine learning require one to be curious, critical and be engaged in lifelong learning. DeZyre offers various courses and certification programmes to help professionals acquire these latest technological skills- data science course and certification being a hot career choice at the moment. Professionals are likely to see a jump of 30-50% in their salaries on mastering these skills.

This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.

Data Science Project in Python- Given his or her job role, predict employee access needs using amazon employee database.

In this project, we are going to work on Sequence to Sequence Prediction using IMDB Movie Review Dataset using Keras in Python.

The goal of this data science project is to build a predictive model and find out the sales of each product at a given Big Mart store.

In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.

In this machine learning and IoT project, we are going to test out the experimental data using various predictive models and train the models and break the energy usage.

Text data requires special preparation before you can start using it for any machine learning project.In this ML project, you will learn about applying Machine Learning models to create classifiers and learn how to make sense of textual data.

Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.

In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.

This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.

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