Data Analytics Projects for Finance
Duration: 2h5m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 974 MB
Genre: eLearning | Language: English
Build a Portfolio of 5 Data Analysis Projects with Descriptionly,Folium,TextBlob,Geopy & Many more & get a job of Data Analyst

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 data ,Analysing Data

Requirements
You will need to install Anaconda. We will show you how to do it in one of the first lectures of the course

Description
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 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

Who this course is for:
Everyone who want to step into Data Science/Data Analytics.

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