Data analytics is the process of taking information from unprocessed data and turning it into meaningful insights. One of the most in-demand skills in the IT (information technology) industry, need for it is seen across a variety of industries, including healthcare, banking, retail, real estate, education, gaming, and more.
Using data to inform future decisions is the goal of data analytics. It is used to assist businesses in resolving issues like lowering risk, enhancing processes, or spotting fraud. Data analytics also places a lot of emphasis on using data visualization to explain your findings to others.
Data analysis is a technique for arranging, gathering or transforming data in order to forecast the future and make informed data-driven decisions. Data analysis also aids in the discovery of potential solutions to business problems. Data analysis covers 6 stages. 1). Ask or Specify Data Requirements 2).Prepare or Collect Data 3).Clean and Process 4).Analyze 5).Share 6),Act or Report
Engaging with data using the industry-standard spreadsheet tool. You can use it to enter, manage, manipulate, analyze, and visualize data.
Calculations, fundamental functions, graphing, formatting, and printing.
Functions like SUMIFs and VLOOKUP.
Using pivot tables, summarize data.
Database sorting and filtering.
Text manipulation: split and connect text, dropdown menus.
Advanced database features like MATCH and INDEX.
Build simple macros.
For goal-seeking and data tables, use what-if analysis.
Data analytics is the process of drawing insightful conclusions from unstructured data
The students will gain knowledge of how to manage and optimize the analytics value chain, which includes gathering and extracting the appropriate values, choosing the proper data processing, and integrating the data from different sources using programming languages like Python. . It is the science of gathering data and discovering insightful patterns, trends, and other useful information.
Data analytics fundamentals.
How to utilize descriptive and inferential statistics.
Using data for forecasting and decision-making.
Today's world and the role of big data
A general-purpose programming language that is useful for creating machine learning algorithms, automating processes, developing apps, and analyzing and visualizing data.
Discover how to manipulate, examine, and visualize complicated datasets using robust open-source Python tools like Git, Pandas, and Matplotlib.
Describe the fundamentals of Python, including types, expressions, and variables.
Apply Python programming using Branching, Loops, Functions, Objects & Classes.
Control flow programming and conditional statements
Object Oriented concepts in details -reusability of code .
Data Structures understanding in Python including Lists, Tuples, Dictionaries, Sets.
Using Matplotlib -Build graphs and visualizations in python
using scikit-learn, Make predictions with linear regression
Work with data in Python using Pandas and NumPy libraries and collect data using API's and web scraping
SQL is a language that is used to communicate with databases. It is an abbreviation for Structured Query Language, which is a method of querying (questioning) the information stored in a database.
Getting Started and Selecting & Retrieving Data with SQL
Filtering, Sorting, and Calculating ,summarize Data with SQL
Identify a subset of data needed from a column or set of columns and write a SQL query to limit to those results
Subqueries and Joins in SQL
Advanced SQL Commands
Modifying and Analyzing Data with SQL
Understanding/Querying Adventure Works Database
Although it is rarely discussed, automation may be the single biggest payback for learning Python. I refer to software that directly substitutes for human labour as "automation." In other words, a computer software that performs things that you would ordinarily perform by hand.
Write scripts that follow industry best practices for syntax and conventions.
Python syntax and program construction
How to schedule your programs to run at specific times.
Diagnose and fix frequent errors.
Python instructions for updating Excel files automatically
How to create scripts that can be used to automate manual tasks,
With a focus on data visualization, the class will begin with the Python libraries NumPy and Pandas before moving on to plotting solutions. You will learn how to use Plotly and Dash Enterprise, a potent tool for constructing dashboards, in addition to more standard plotting tools like Matplotlib and Seaborn.
Prepare and present a data story
collect, analyze, and/or modify data from a variety of sources.
Investigate data stories with exploratory data analysis.
Utilize Pandas and NumPy to manipulate data.
Utilize the cutting-edge Python visualization tools Plotly and Dash
Create a dashboard
Create expert data science solutions by abiding by the rules of powerful dashboard design.
Set up the dashboard on a live server and launch your project.
Making a computer learn by analyzing data and statistics is known as machine learning.
The development of machine learning is a step toward artificial intelligence (AI).
A program that analyzes data and learns to predict outcomes is known as machine learning.
utilizing the scikit-learn library to apply machine learning algorithms like logistic regression and random forest.
Choosing quality features to feed your algorithms.
separating data into training, test, and cross-validation sets appropriately.
How to use the Pandas library to clean and balance your data.
Important theoretical ideas including bias, variance, and overfitting.
Assessing the performance of your machine learning models.
You will learn how to create visualizations, organize data, and design dashboards in order to make more informed business decisions.
You'll understand about statistics, data mapping, and building data connections.
Manipulating data in Tableau
Advanced visualization tools
Building Dashboards & Creating Stories
Publication & Distribution of your visualization
Intro to data maps
Bulding visualization maps
Advanced data manipulations
Building custom charts
Module 1: Introducing Microsoft Power Bi Desktop
Meet Power BI Desktop
Downloading Power BI
Power BI Desktop Interface & Workflow
Resources & Monthly Updates
Introduction to the Project 1 dataset
Preliminary visualization and dashboard development
Module 2: Connecting & Shaping Data
Power BI Front-end and Back-end
Types of data connectors
Introduction to Power Query Editor Layout
Index and conditional column
Grouping & Aggregating
Pivoting & Un-pivoting
Merging Queries & Joins
Data source setting and refreshing queries
Folders and append queries
Advanced queries, M Language, and customisation
Module 3: Data Modelling
Fact Table & Dim Table
Primary, Foreign & Composite Key
Cardinality – 121, 12Many & Many21, Many2Many
Cross Filter Direction & Security
Data Model Diagrams and how Visuals interact based on the relationships
Star & Snowflake Schemas
Introduction to Project 2
Module 4: DAX
Dax v/s M Language
Introduction to Project 3
DAX as calculated columns
DAX as measures
Time intelligence functions
Dedicated Measure Tables
DAX Syntax & Operators
Maths & Stats Operations
Data Modelling & DAX
Data & Time
TopN Dynamic DAX Functions
DAX for date difference excluding weekends
DAX Best Practice
Module 5: Visualizing data with reports
Module 6: Power Bi Service
Introduction to Pro & Premium Capacity
Publishing a dashboard
Workspace & Gateways
Sharing dashboard and managing permissions
Connecting to advanced JSON Files
Introduction to data mart
Introduction to Project 4
Module 7: Bonus Lecture | Power BI, AI, Microsoft Fabrics & Chat GTP – Prototype Introduction