Data Analysts aim to discover how data can be used to answer questions and solve problems through the use of technology. Many believe this will be the job of the future and be the single most important skill a job application can have in 2020.

In the last two decades, the pervasiveness of the internet and interconnected devices has exponentially increased the data we produce. The amount of data available to us is Overwhelming and Unprecedented. Obtaining, transforming and gaining valuable insights from this data is fast becoming the most valuable and in-demand skill in the 21st century.

In this course, you'll learn how to use Data, Analytics, Statistics, Probability, and basic Data Science to give an edge in your career and everyday life. Being able to see through the noise within data, and explain it to others will make you invaluable in any career.

We will examine over 2 dozen real-world data sets and show how to obtain meaningful insights. We will take you on one of the most up-to-date and comprehensive learning paths using modern-day tools like Python, Google Colab and Google Data Studio.

You'll learn how to create awesome Dashboards, tell stories with Data and Visualizations, make Predictions, Analyze experiments and more!

Our learning path to becoming a fully-fledged Data Analyst includes:

1. The Importance of Data Analytics

2. Python Crash Course

3. Data Manipulations and Wrangling with Pandas

4. Probability and Statistics

5. Hypothesis Testing

6. Data Visualization

7. Geospatial Data Visualization

8. Story Telling with Data

9. Google DataStudio Dashboard Design - Complete Course

10. Machine Learning - Supervised Learning

11. Machine Learning - Unsupervised Learning (Clustering)

12. Practical Analytical Case Studies

Google Data Studio Dashboard &Visualization Project:

1. Executive Sales Dashboard (Google Data Studio)

Python, Pandas &Data Analytics and Data Science Case Studies:

1. Health Care Analytics & Diabetes Prediction

2. Africa Economic, Banking & Systematic Crisis Data

3. Election Poll Analytics

4. Indian Election 2009 vs 2014

5. Supply-Chain for Shipping Data Analytics

6. Brent Oil Prices Analytics

7. Olympics Analysis - The Greatest Olympians

8. Home Advantage Analysis in Basketball and Soccer

9. IPL Cricket Data Analytics

10. Predicting the Soccer World Cup

11. Pizza Resturant Analytics

12. Bar and Pub Analytics

13. Retail Product Sales Analytics

14. Customer Clustering

15. Marketing Analytics - What Drives Ad Performance

16. Text Analytics - Airline Tweets (Word Clusters)

17. Customer Lifetime Values

18. Time Series Forecasting - Demand/Sales Forecast

19. Airbnb Sydney Exploratory Data Analysis

20. A/BTesting