Grokking Data Science

Grokking Data Science

Master the skills that can get you a $100K+ salary even if you bunked your statistics classes.

This course is your comprehensive guide to getting your start as a data scientist. No need to waste hours and hours on browsing from one article to the next and piecing together the info you need to grasp important topics. 

No need to get overwhelmed by the information overload. Find easy to follow, hands-on, and fun explanations of all the essential topics in one place so you can quickly and efficiently learn what you need to thrive as a data scientist.

Details

"I want to understand this data science concept. Let me Google it". Then after hours of surfing, reading random articles, and invoking the heavens, you are more confused than before.

or

"Data science is the sexiest and highest paying job of the 21st century. I want to become a data scientist too".

Is that you? If yes, you are at the right place.

Every other person is trying to put together the pieces of the puzzle that can land them the hottest job of the century. I mean who doesn't want to be the most sought out professional in the industry. 

There are tons of data science resources out there too. Still not many succeed. Why?

There is so much information.  But chances are that either it's written as if for an academic publication -- "I need to understand a concept and not do research on the topic. 

Keep it simple and intuitive!" -- or just for the sake of writing that article with some fancy jargon thrown here and there.

You know how many articles I had to read before understanding something simple like p-value in an intuitive way? I couldn't just find a decent explanation. This is exactly why I decided to create this course.

This is why I am here to help you. I want you to avoid wasting time in browsing from one article to another.

I wish I had all the information I needed in one place to make my learning curve faster.

"Top athletes have coaches and trainers. Mountain climbers have Sherpas. They don’t do it alone. Why would you?"

Information overload. Siloed resources. Non-methodical approach. Don't let these become a barrier in your journey and kick-start your efforts to becoming a great data scientist with solid foundations.

You deserve the best help! I’d like to invite you to learn data science concepts with me.

This course is for you if:

• You have the basic knowledge of Python.
• You have a lot of willingness to learn.
• You are committed to become a great data scientist and get that high-paying job.

The best you can do is to invest in yourself today. The decision is now yours. I hope to see you inside!

P.S. Remember that taking this course is completely risk-free, with 100% money-back guarantee. I'm here with the goal of providing solid value. If after going through the course, you do not find value from it, I insist that you take your money back.

P.P.S. This is what Grokking Data Science students are saying:

I just happened to complete your 'Grokking Data Science' course. I have read many books and still remained unclear as to where to start and end. Your coursework was so clear and helped fill the gap.
 
Thanks a lot for giving me an amazing learning experience." - Barani (Data Analyst Consultant)

Contents

1. Python Fundamentals for Data Science

• Creating the Workspace - Jupyter Notebooks
• Python Libraries
• Learning NumPy - An Introduction
• NumPy Basics - Creating NumPy Arrays and Array Attributes
• NumPy Basics - Array Indexing and Slicing
• NumPy Basics - Reshaping and Concatenation
• NumPy Arithmetic and Statistics - Computations and Aggregations
• NumPy Arithmetic and Statistics - Comparison and Boolean Masks
• Exercises: NumPy
• Learning Pandas - An Introduction
• Pandas Core Components - The Series Object
• Pandas Core Components - The DataFrame Object
• Pandas DataFrame Operations - Read, View and Extract Information
• Pandas DataFrame Operations - Selection, Slicing, and Filtering
• Pandas DataFrame Operations - Grouping and Sorting
• Pandas DataFrame Operations - Dealing With Missing and Duplicates
• Pandas DataFrame Operations - Pivot Tables and Functions
• Pandas: Further Readings and Cheat Sheet
• Exercises: Pandas
• Data Visualization - An Introduction
• Data Visualization - Matplotlib Tips
• Data Visualization Techniques - Scatter, Line, and Histogram
• Data Visualization Techniques - Bar and Box Plot
• Data Visualization Cheat Sheet
• Quiz: Data Visualization

2. The Fundamentals of Statistics

• Introduction
• Statistical Features - Basics
• Statistical Features - Working With Box Plots
• Basics of Probability
• Bayesian Statistics
• Probability Distributions - An Introduction
• Types of Distributions - Uniform, Bernoulli, and Binomial
• Types of Distributions - Normal
• Types of Distributions - Poisson and Exponential
• Probability Distributions Recap
• Statistical Significance
• Quiz: Statistics

3. Machine Learning 101

• Introduction
• Understanding Machine Learning
• Types of Machine Learning Algorithms
• Machine Learning Algorithms I
• Machine Learning Algorithms II
• Quiz: Machine Learning Algorithms
• Evaluating a Model
• Quiz: Evaluating a Model
• Key Points to Remember
• Machine Learning Project Checklist

4. End-to-End Machine Learning Project

• Introduction
• Kaggle Challenge - Exploratory Data Analysis
• Kaggle Challenge - Data Preprocessing
• Kaggle Challenge - Data Transformation
• Kaggle Challenge - Machine Learning Models
• Kaggle Challenge - Fine Tune Parameters
• Kaggle Challenge - Present, Launch and Maintain the System
• Assignment
• Further Study Material

5. The Real Talk

• How to Get That High-Paying Job
• Imposter Syndrome
• Final Thoughts
  

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