On successful completion of the prework, you will
- Be familiar with the GreyAtom style of learning.
- Have all the necessary packages installed in your system.
- Have a fair intution about what Data Science is.
- Revise the basics of programming in Python.
- Have the basic knowledge of Maths & Stats that would be used ahead.
GreyAtom’s hands on learning approach, diversity and commitment to academic excellence will make sure that your learning outcomes are extremely high. Before you begin your journey with GreyAtom, get yourself accquainted with the our philosophy. Expose yourself to how to keep up the pace throughout the learning journey.
- Learning Data Science - the GreyAtom way - 50 mins
Looking forward to the handson learning experience, it is very important to have your system ready for all the coding tasks/ assignments that you would be attempting. Install the necessary packages and set up your system before you come for the sessions.
Data Science can add lots of value to any business and has many interesting roles. Before starting your Data Science journey, explore more about the same, so that you develop a clear intuition by reading the blogs below and covering the topic on our platform GLabs.
- What is Data Science? - 7 mins
- Is Data Science a bubble? - 5 mins
- One important element of Data Science is Machine Learning. Let's understand the same. Machine Learning 101 - 5 mins
- Learn what Data Science means, what it can do, and what it can't do along with an overview of the Data Science pipeline Introduction to Data Science - 145 mins
To realize the power of Data Science applications, programming is the tool. Make sure you have a strong foundation here. The first blog will help you understand what is programming and get started with your first program in this learning journey on GLabs.
- What is Programming? - 3 mins
- Programming Language for Data Science - 5 mins
- Learn in brief what a computer program is, it's basic structure and how to implement it. Introduction to Programming - 90 mins
After we have seen our first program in Python, time to explore more. Though Python is a high level programming language and can solve complex tasks, it is easy to learn and has clean syntax. Let's get started.
- Explore Python step by step here - 120 mins
- The best way to learn something is by doing it. Refer to the Python examples here and solve them on your own.
Next topic we want you to look into is the Mathematics in Data Science. Maths & Stats is the heart of Data Science. Learn the fundamentals of essential Maths & Stats before diving deep.
Let's begin with understanding different types of Data.
- Structured & Unstructured Data - 5 mins
- Different Scales of Measuring Data - 10 mins
Every data has some story to tell. We use different kinds of plots to visualize the hidden pattern/insights in the data. Understand the different kinds of plots and what insights they give.
- Different kinds of plots - 10 mins
To summarize the information contained in a data, we have Descriptive Statistics. Master basic Statistical tools/concepts that would be extensively used further.
- Understanding Descriptive Statistics with Examples - 15 mins
- Understanding Statistics - 10 mins
- Statistics Fundamentals - 85 mins (suggested to watch at 1.5x speed)
- Histogram - 3 min
- Pie Chart & Bar Chart - 2 min
- Boxplots - 2 mins
- Quantiles & Percentiles - 5 mins
- What is Statistical Distribution? - 4 min
- Binomial Distribution - 10 mins
- Normal Distribution - 4 mins
- Population Parameters - 10 mins
- Mean, Variance and Standard Deviation - 10 mins
- Covariance & Correlation - Part 1 - 15 mins
- Covariance & Correlation - Part 2 - 12 mins
- Statistical Models - 3 mins
- Sampling Distribution - 5 mins
Data Science is all about making predictions. And what comes to our rescue in making predictions is Probability. Learn fundamentals of Probability.
- Introduction to Probability - 60 mins
- Understand the concepts of probability probability through the example of flipping a quarter and rolling a die. Theoritical Probability - 50 mins
- Understand the most commonly used probability distributions used in Data Science. - 30 mins
While we make predictions, if there is a change in the input, it influences the output. A solid understanding of Multivariate Calculus is very important to understand the change.
- Learn the fundamentals of Multivariate Calculus- 150 mins
Completing the above items is the bare minimum for you to start the program. Once you have completed the above items, we strongly recommend you to cover the below as well so that you sail through the program smoothly.
- Get exposed to the nuts and bolts of Python - a popular programming language of choice for Data Scientists. Getting Started with Python - 150 mins
- Explore deeper into Python to handle the complete program flow - file I/O, control structures and errors which are important for effective programming. Handling Program Flow in Python - 240 mins
- To check your skills, pick up challenges and solve here - 120 mins
- Try answering the questions here - 30 mins
- Dig deeper into the Math behind Multivariate Calculus - 120 mins
- Learn about different kinds of data that you will come across while working on data sets throughout the program. We will then proceed and learn about techniques to summarize the data visually and mathematically in order to build insights out of them. Descriptive Statistics Fundamentals - 60 mins
- Learn about basic concepts of probability that we will need for understanding algorithms. Probability Fundamentals - 90 mins