Data Analytics using Python

• Master analytical skills.
• Learn from the best & get mentored by an industry expert.
• Land your dream job in the dynamic analytics industry.
• Be able to Code in Python and solve Data Analysis problem.
• Participate in Kaggle competitions & hackatons.

Exploratory Data Analysis with Seaborn and Pandas

Exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. It is used to understand the data, get context about it, understand the variables and the relationship between them, and formulate hypothesis that could be useful when building predictive models.

Assignment_1:EDA with Python Pandas (30-45 mins)

In this Assignment, you will do-&-learn:

• importing datasets, dealing with missing values, changing data types.
• filtering, sorting, selecting specific column(s).
• dealing with duplicate values, dropping and adding rows and columns.
• counting values, counting unique values.

Assignment_2: Data Analysis of Customer Attrition(45 – 60 mins)
Telephone service companies, Internet service providers, pay TV companies, insurance firms, and alarm monitoring services, often use customer attrition analysis and customer attrition rates as one of their key business metrics because the cost of retaining an existing customer is far less than acquiring a new one. Companies from these sectors often have customer service branches which attempt to win back defecting clients, because recovered long-term customers can be worth much more to a company than newly recruited clients.

Assignment_3: Data Analysis of Customer Churn rate at a Telecom Co.

(60-75mins)
Analyzing a dataset on the churn rate of telecom operator clients.
Note : This dataset is different from the one’s used in the earlier Assignment

Module 6_A: Time Series Analysis – Introduction & terminology

Module 6_B: Time Series Analysis – Application in real-time

Module 6_C: Time Series Forecasting – Concepts & Problem Definition

Module 6_D: Time Series Forecasting – Problem Solving

Module_6deals with predicting the electricity consumption of a household for the next three months, estimating traffic on roads at certain periods, and predicting the price at which a stock will trade on the BSE or NSE.

They all fall under the concept of time series data. You cannot accurately predict any of these results without the ‘time’ component. And as more and more data is generated in the world around us, time series forecasting keeps becoming an ever more critical technique for a data analyst or business analyst to master.

Compulsory Projects to be done as a part of course.