Data Analytics

Data Analytics

Data helps in 

1) Make better decisions

2) Solve problems by finding the reason for it

3) To evaluate, improve and benchmarking process

4) To understand consumers (their preferences) and the market 

Data Analytics - The scientific process of transforming data into insights for making better decisions.

It is about what will happen in the future and how we can improve it.

Data Analysis - A kind of postmortem analysis - what has happened in the past, why it happened etc.

Types of Data Analytics -

The level of difficulty and value added by it increases from top to bottom.

1) Descriptive Analytics - what happened

2) Diagnostic Analytics - why it happened 

3) Predictive Analytics - what will happen, forecasting trends 

4) Prescriptive Analytics - how can we make it happen

Skill set required to be a good data analyst are:

1) Mathematics 2) Hacking skills (Technology)  3) Business and strategy acumen - Domain knowledge. 

One person may not have all, so we need a group of people working together. 

Why Python? 

It is very simple and versatile programming language with extensive libraries. And it can also be embedded into programs written in other languages. 

It can be used for building many types of applications like: 

1. Desktop apps

2. Web apps

3. Database apps

4. Networking apps

5. Data science 

6. Machine Learning

7. IoT and Embedded Systems

Differenet types of Variables i.e. DATA - can be :

1. Categorical - Defined categories

1. Nominal data: No ranks like Gender, color, etc.

2. Ordinal data: Ranked data like grades A, B, C 

2. Numerical - 

1. Discrete - Countable items like no. of children 

2. Continuous - Measured quantities like weight, voltage, salary.

1. Interval Scale - The difference is meaningful but there is no 'zero point' on the scale, like year, tempeartue.

2. Ratio Scale - The difference is meaningful and there is a 'zero point' too like weight, salary, age.

Python for Data Analytics

1 Numpy and Data Manipulation

- Introduction to Numpy - Installation of Jupyter Notebook - Creation, Indexing, and Slicing of Numpy Arrays - Mathematical Operations, Combining and Splitting Arrays - Search, Sort, Filter Arrays, Aggregating Functions - Statistical Functions in Arrays 2 Pandas for Data Analysis - Introduction to Pandas - Creation of Data Frames, Exploring Data - Dealing with Duplicate values, Missing Data - Column Transformation in Pandas, GroupBy - Merge, Concatenate, Join in Pandas - Pivoting and Melting Dataframes 3 Matplotlib for Data Visualization - Introduction to Matplotlib - Bar, Line, Scatter, Pie, Box, Histogram, Violin, Stem plots - Stack Plot, Step Chart, Legends, Subplot - Save a Chart Using Matplotlib - Data Visualization in Seaborn - Seaborn Project

DATA ANALYTICS 4 MySQL for Data Analytics - Introduction to MySQL - Installation, Importing Data - Select Query, Where Clause - AND, OR, NOT Operators, Like Operator - Order By, Limit, Between - IN, NOT IN operator, String Functions - Data Aggregation, Numeric Functions - Date Functions, Case Operator - Group By, Having Clause - Joins, Set Operators, Subqueries, Views - Stored Procedure, Window Functions 5 Excel for Data Analytics - Introduction to MS Excel - Basic Functions, Data Validation - Data Connectors, Conditional Formatting - Basics of Formatting, Sorting, Filtering Data - Dealing with Null Values, Duplicate Values - Trimming Whitespaces, Text Functions - IF, AND, OR Functions, Date & Time Functions - COUNTIF, SUMIF Functions, Xlookup - Power Query, Cleaning and Transformation - Creating a Dashboard in Excel, myExcel Project 6 Power BI Essentials - Introduction to Power Bi - Installation of Power Bi Desktop - Data Connectors, Basic Transformations - Format Tool, Pivoting, Unpivoting of data - Adding Conditional Columns, Merge queries - Data model, Relationship Management - Introduction to DAX, Calculated Columns, Measures - DAX Functions, Visualizations, Filters - Creating Reports, Custom Visuals - Designing for Phone vs Desktop Report Viewers - Publishing Reports to Power BI Services