Certified Data Analytics Associate with Python (hybrid)

Module 1 – Introduction To Data Science And Data Science Libraries

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Data science is the field of applying advanced analytics techniques and scientific principles to extract valuable information from data for business decision-making, strategic planning and other uses. Through this module, you will learn the basics, how to analyze data, and then create some beautiful visualizations using Python.

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Numpy

It’s a general-purpose array-processing package that provides high-performance multidimensional objects called arrays and tools for working with them. NumPy also addresses the slowness problem partly by providing these multidimensional arrays as well as providing functions and operators that operate efficiently on these arrays.

• NumPy Getting Started

• NumPy Creating Arrays

• NumPy Array Indexing

• NumPy Array Slicing

• NumPy Data Types

• NumPy Copy vs View

• NumPy Array Shape

• NumPy Array Reshape

• NumPy Array Iterating

• NumPy Array Join

• NumPy Array Split

• NumPy Array Search

• NumPy Array Sort

• NumPy Array Filter

• NumPy Random

• NumPy Inbuilt Methods

Module 2 – Pandas

Pandas is an important library in Python for Data Science. It is used for data manipulation and analysis.  It is well suited for different data such as tabular, ordered and unordered time series, matrix data, etc.

• Pandas Getting Started

• Pandas Series

• Pandas DataFrames

• Pandas Read CSV

• Pandas Read JSON

• Pandas Read Excel

• Pandas Analyzing Data

Module 3 – Data Cleaning And Data Wrangling Using Python Pandas

Data scientists spend a large amount of their time cleaning datasets and getting them down to a form with which they can work. In fact, a lot of data scientists argue that the initial steps of obtaining and cleaning data constitute 80% of the job. Therefore, if you are just stepping into this field or planning to step into this field, it is important to be able to deal with messy data, whether that means missing values, inconsistent formatting, malformed records, or nonsensical outliers. In this module, we’ll leverage Python’s Pandas to clean data.

• Cleaning a DataFrame

• Removing Columns

• Removing Rows

• Filling Missing Values

• Improving Readability

• Dropping Columns in a DataFrame

• Changing the Index of a DataFrame

Module 4 – Matplotlib Visualization with Python

Matplotlib is a python library used to create 2D graphs and plots by using python scripts. It has a module named pyplot which makes things easy for plotting by providing feature to control line styles, font properties, formatting axes etc. It supports a very wide variety of graphs and plots namely - histogram, bar charts, power spectra, error charts etc

• Python Data Visualization

• Python Chart Properties

• Python Chart Styling

• Python Box Plots

• Python Heat Maps

• Python Scatter Plots

• Python Line Charts

• Python Pie Charts

• Python Bar Charts

• Python Time Series

• Python Geographical Data

Module 5 – Python seaborn Library

Seaborn is one of an amazing library for visualization of the graphical statistical plotting in Python. Seaborn provides many color palettes and defaults beautiful styles to make the creation of many statistical plots in Python more attractive. Seaborn library aims to make a more attractive visualization of the central part of understanding and exploring data. It is built on the core of the matplotlib library and also provides dataset-oriented APIs.

• Plotting Chart Using seaborn Library

• Line plot

• Dist plot

• Lmplot

• Histogram

• Bar Plot

• Count Plot

• Point Plot

• Violin Plot

• Heatmap

Module 6 –  Statistics

• What is statistics?

• Basic terminology of statistics

• Types of statistics

• Descriptive statistics

• Measure of Central Tendency ( Mean, median, mode )

• Measures of Dispersion ( Variance, Standard Deviation, Range-its derivation )

• Inferential statistics

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Module 6 – Exploratory Data Analysis

In this module, you will learn what is meant by exploratory data analysis, and you will learn how to perform computations on the data to calculate basic descriptive statistical information, such as mean, median, mode, and quartile values, and use that information to better understand the distribution of the data. You will learn about putting your data into groups to help you visualize the data better. Exploratory data analysis (EDA) is an especially important activity in the routine of a data analyst or scientist. It enables an in depth understanding of the dataset, define or discard hypotheses and create predictive models on a solid basis. It uses data manipulation techniques and several statistical tools to describe and understand the relationship between variables and how these can impact business.

Capstone Project 1 : Credit Card Fraud Detection Case Study

Overview : Lots of financial losses are caused every year due to credit card fraud transactions, the financial industry has switched from a posterior investigation approach to an a priori predictive approach with the design of fraud detection algorithms to warn and help fraud investigators.

This case study is focused to give you an idea of applying Exploratory Data Analysis (EDA) in a real business scenario. In this case study, apart from applying the various Exploratory Data Analysis (EDA) techniques, you will also develop a basic understanding of risk analytics and understand how data can be utilized in order to minimize the risk of losing money while lending to customers.

Capstone Project 2 :  Customer Churn Prediction

Overview : When clients stop doing business with a company, this is known as customer churn or customer attrition.

Because the cost of getting a new customer is usually higher than keeping an existing one, understanding customer churn is critical to a company’s success. As a result, churn analysis is the first step in gaining a better understanding of your clients.

In this , we tried to analyze customer behaviour.  

Module 7 – Model Development

In this module, you will learn how to define the explanatory variable and the response variable and understand the differences between the simple linear regression and multiple linear regression models. You will learn how to evaluate a model using visualization and learn about polynomial regression and pipelines. You will also learn how to interpret and use the R-squared and the mean square error measures to perform in-sample evaluations to numerically evaluate our model. And lastly, you will learn about prediction and decision making when determining if our model is correct.