Build a Portfolio of 5 Data Analysis Projects with Python, Seaborn, Pandas, Plotly, Folium, TextBlob ,Geopy & Many more
What you’ll learn
- Get a job as a data Analyst on an average $156,000 after showcase these Projects on your Resume
- By the end of this course you will understand the inner workings of the data analytics pipeline – joining,manipulating,filtering, extracting
- You will need to install Anaconda. We will show you how to do it in one of the first lectures of the course
This is the first course that gives hands-on Data Analysis Projects using Python..
Can you start right now?
A frequently asked question of Python Beginners is: “Do I need to become an expert in Python coding before I can start working on Data Analysis Projects?”
The clear answer is: “No!
- You just require some Python Basics like data types, simple operations/operators, lists and numpy arrays that you can learn from my Free Python course ‘Basics Of Python’
As a Summary, if you primarily want to use Python for Data Science or as a replacement for Excel, then this course is a perfect match!
Why should you take this Course?
- It explains Projects on real Data and real-world Problems. No toy data! This is the simplest & best way to become a Data Analyst/Data Scientist
- It shows and explains the full real-world Data. Starting with importing messy data, cleaning data,merging and concatenating data, grouping and aggregating data, Exploratory Data Analysis through to preparing and processing data for Statistics, Machine Learning and Data Presentation.
- It gives you plenty of opportunities to practice and code on your own. Learning by doing.
- In real-world projects, coding and the business side of things are equally important. This is probably the only course that teaches both: in-depth Python Coding and Big-Picture Thinking like How you can come up with a conclusion
- Guaranteed Satisfaction: Otherwise, get your money back with 30-Days-Money-Back-Guarantee.
Who this course is for:
- Everyone who want to step into Data Science/Data Analytics.
- Anyone interested about the rapidly expanding world of data Analytics/Data Science
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