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## Data Science: Step by Step – A Resource for LPBI Group’s One-Year Internship in IT, IS, DS

### Data Science: Step by Step – A Resource for LPBI Group One-Year Internship in IT, IS, DS

Reporter: Aviva Lev-Ari, PhD, RN

### 9 free Harvard courses: learning Data Science

In this article, I will list 9 free Harvard courses that you can take to learn data science from scratch. Feel free to skip any of these courses if you already possess knowledge of that subject.

### Step 1: Programming

The first step you should take when learning data science is to learn to code. You can choose to do this with your choice of programming language?—?ideally Python or R.

If you’d like to learn R, Harvard offers an introductory R course created specifically for data science learners, called Data Science: R Basics.

This program will take you through R concepts like variables, data types, vector arithmetic, and indexing. You will also learn to wrangle data with libraries like dplyr and create plots to visualize data.

If you prefer Python, you can choose to take CS50’s Introduction to Programming with Python offered for free by Harvard. In this course, you will learn concepts like functions, arguments, variables, data types, conditional statements, loops, objects, methods, and more.

Both programs above are self-paced. However, the Python course is more detailed than the R program, and requires a longer time commitment to complete. Also, the rest of the courses in this roadmap are taught in R, so it might be worth learning R to be able to follow along easily.

### Step 2: Data Visualization

Visualization is one of the most powerful techniques with which you can translate your findings in data to another person.

With Harvard’s Data Visualization program, you will learn to build visualizations using the ggplot2 library in R, along with the principles of communicating data-driven insights.

### Step 3: Probability

In this course, you will learn essential probability concepts that are fundamental to conducting statistical tests on data. The topics taught include random variables, independence, Monte Carlo simulations, expected values, standard errors, and the Central Limit Theorem.

The concepts above will be introduced with the help of a case study, which means that you will be able to apply everything you learned to an actual real-world dataset.

### Step 4: Statistics

After learning probability, you can take this course to learn the fundamentals of statistical inference and modelling.
This program will teach you to define population estimates and margin of errors, introduce you to Bayesian statistics, and provide you with the fundamentals of predictive modeling.

### Step 5: Productivity Tools (Optional)

I’ve included this project management course as optional since it isn’t directly related to learning data science. Rather, you will be taught to use Unix/Linux for file management, Github, version control, and creating reports in R.

The ability to do the above will save you a lot of time and help you better manage end-to-end data science projects.

### Step 6: Data Pre-Processing

The next course in this list is called Data Wrangling, and will teach you to prepare data and convert it into a format that is easily digestible by machine learning models.

You will learn to import data into R, tidy data, process string data, parse HTML, work with date-time objects, and mine text.

As a data scientist, you often need to extract data that is publicly available on the Internet in the form of a PDF document, HTML webpage, or a Tweet. You will not always be presented with clean, formatted data in a CSV file or Excel sheet.

By the end of this course, you will learn to wrangle and clean data to come up with critical insights from it.

### Step 7: Linear Regression

Linear regression is a machine learning technique that is used to model a linear relationship between two or more variables. It can also be used to identify and adjust the effect of confounding variables.

This course will teach you the theory behind linear regression models, how to examine the relationship between two variables, and how confounding variables can be detected and removed before building a machine learning algorithm.

### Step 8: Machine Learning

Finally, the course you’ve probably been waiting for! Harvard’s machine learning program will teach you the basics of machine learning, techniques to mitigate overfitting, supervised and unsupervised modelling approaches, and recommendation systems.

### Step 9: Capstone Project

After completing all the above courses, you can take Harvard’s data science capstone project, where your skills in data visualization, probability, statistics, data wrangling, data organization, regression, and machine learning will be assessed.

With this final project, you will get the opportunity to put together all the knowledge learnt from the above courses and gain the ability to complete a hands-on data science project from scratch.

Note: All the courses above are available on an online learning platform from edX and can be audited for free. If you want a course certificate, however, you will have to pay for one.

### 8 Free MIT Courses to Learn Data Science Online

enrolled into an undergraduate computer science program and decided to major in data science. I spent over \$25K in tuition fees over the span of three years, only to graduate and realize that I wasn’t equipped with the skills necessary to land a job in the field.

I barely knew how to code, and was unclear about the most basic machine learning concepts.

I took some time out to try and learn data science myself — with the help of YouTube videos, online courses, and tutorials. I realized that all of this knowledge was publicly available on the Internet and could be accessed for free.

It came as a surprise that even Ivy League universities started making many of their courses accessible to students worldwide, for little to no charge. This meant that people like me could learn these skills from some of the best institutions in the world, instead of spending thousands of dollars on a subpar degree program.

In this article, I will provide you with a data science roadmap I created using only freely available MIT online courses.

### Step 1: Learn to code

I highly recommend learning a programming language before going deep into the math and theory behind data science models. Once you learn to code, you will be able to work with real-world datasets and get a feel of how predictive algorithms function.

MIT Open Courseware offers a beginner-friendly Python program for beginners, called Introduction to Computer Science and Programming.

This course is designed to help people with no prior coding experience to write programs to tackle useful problems.

### Step 2: Statistics

Statistics is at the core of every data science workflow — it is required when building a predictive model, analyzing trends in large amounts of data, or selecting useful features to feed into your model.

MIT Open Courseware offers a beginner-friendly course called Introduction to Probability and Statistics. After taking this course, you will learn the basic principles of statistical inference and probability. Some concepts covered include conditional probability, Bayes theorem, covariance, central limit theorem, resampling, and linear regression.

This course will also walk you through statistical analysis using the R programming language, which is useful as it adds on to your tool stack as a data scientist.

Another useful program offered by MIT for free is called Statistical Thinking and Data Analysis. This is another elementary course in the subject that will take you through different data analysis techniques in Excel, R, and Matlab.

You will learn about data collection, analysis, different types of sampling distributions, statistical inference, linear regression, multiple linear regression, and nonparametric statistical methods.

### Step 3: Foundational Math Skills

Calculus and linear algebra are two other branches of math that are used in the field of machine learning. Taking a course or two in these subjects will give you a different perspective of how predictive models function, and the working behind the underlying algorithm.

To learn calculus, you can take Single Variable Calculus offered by MIT for free, followed by Multivariable Calculus.

Then, you can take this Linear Algebra class by Prof. Gilbert Strang to get a strong grasp of the subject.

All of the above courses are offered by MIT Open Courseware, and are paired with lecture notes, problem sets, exam questions, and solutions.

### Step 4: Machine Learning

Finally, you can use the knowledge gained in the courses above to take MIT’s Introduction to Machine Learning course. This program will walk you through the implementation of predictive models in Python.

The core focus of this course is in supervised and reinforcement learning problems, and you will be taught concepts such as generalization and how overfitting can be mitigated. Apart from just working with structured datasets, you will also learn to process image and sequential data.

MIT’s machine learning program cites three pre-requisites — Python, linear algebra, and calculus, which is why it is advisable to take the courses above before starting this one.

### Are These Courses Beginner-Friendly?

Even if you have no prior knowledge of programming, statistics, or mathematics, you can take all the courses listed above.

MIT has designed these programs to take you through the subject from scratch. However, unlike many MOOCs out there, the pace does build up pretty quickly and the courses cover a large depth of information.

Due to this, it is advisable to do all the exercises that come with the lectures and work through all the reading material provided.

SOURCE

Natassha Selvaraj is a self-taught data scientist with a passion for writing. You can connect with her on LinkedIn.

https://www.kdnuggets.com/2022/03/8-free-mit-courses-learn-data-science-online.html