Data Analytics

What is Data Analytics?

Data analytics is the process of collecting, cleaning, transforming, and organizing raw data to uncover patterns, derive meaningful insights, and make informed decisions. It uses various techniques, from statistical analysis to machine learning, to analyze past and present data to predict future trends and solve problems.

Skills Required for Data Analytics?

There are multiple skills which are required to be a Data analyst. Some of the main skills are mentioned below:

• Some of the common programming languages which are used are R and Python.
• For databases Structured Query Language (SQL) is a programming language used.
• Machine Learning is used in data analysis.
• In order to better analyse and interpret probability and statistics are used.
• For collecting and organising data, Data Management is used in data analysis.
• To use charts and graphs Data visualisation is used.

Key responsibilities of a Data Analyst?

• Data collection and preparation:
  Gathering data from various sources, cleaning it for accuracy, and performing exploratory analysis to find patterns.

• Analysis and interpretation:
  Using statistical methods to analyze datasets, and interpret complex data to solve specific business problems.

• Reporting and visualization:
  Creating and presenting visualizations like dashboards and charts to stakeholders, and preparing analytical reports.

• Collaboration:
  Working with cross-functional teams to define KPIs, and with management to prioritize business needs.

• Database management:
  Designing and maintaining databases and using SQL to extract and manipulate data.

• Module 1

  Excel for Data Analytics, Advanced SQL, Python Programming (Core), Basic Statistics

  • Excel interface, formatting, data types
  • Functions: Text, Logical, Lookup
  • Aggregation: SUM, COUNT, AVERAGE, IF variants
  • Pivot Tables, Charts, Conditional Formatting
  • Dashboards (basic level)
  • Joins, Subqueries, Set Operators
  • Triggers, Views, Stored Procedures
  • Practice in MySQL/PostgreSQL
  • Variables, Data Types, Operators, Control Flow
  • Functions, Lists, Tuples, Dicts, Sets
  • Descriptive Stats: Mean, Median, Variance, Std Dev
  • Trend Analysis, Probability Basics
  • Normal & Binomial Distribution
  • Permutations & Combinations
• Module 2

  SQL + Python Programming

  • Joins, Subqueries, Set Operators
  • Practice in MySQL/PostgreSQL
  • Triggers, Views, Stored Procedures

  Python Programming (Core)

  • Variables, Data Types, Operators, Control Flow
  • Functions, Lists, Tuples, Dicts, Sets
  • Mini Project #2: Student Management System (SQL + Python queries -Personal Builds)
• Module 3

  Python for Analytics + Power BI

  • Product Sales Dashboard using Python + Power BI
  • (Personal Builds)
  • NumPy: Arrays, Math operations
  • Pandas: DataFrames, Cleaning, Merging, Grouping
  • Visualization: Matplotlib, Seaborn

  Python for Data Analysis Machine Learning Concept Power BI Essentials

  • Data connection: Excel, CSV, SQL Server
  • Modeling, Visuals, Filters, DAX basics
  • Dashboards & Publishing
  • Supervised learning - Linear Regression,Logistics
  • Regression, Decision Tree, Random forest, KNN,
  • Boosting Algorithm, SVM, KFold, Naive Bayes
  • Unsupervised Learning - KMeans, Hierarchical Clustering, PCA

  Power BI Essentials

  • Data connection: Excel, CSV, SQL Server
  • Modeling, Visuals, Filters, DAX basics
  • Dashboards & Publishing
• Module 4

  Tableau + Advanced Analytics + Git

  Connecting to data, Visuals: Bar, Map, Heatmap Parameters, Calculated Fields Story Points, Forecasting, Clustering

  Tableau Fundamentals Advanced Analytics

  • Time Series Forecasting, Outlier Detection
  • ML Intro: Linear Regression, K-Means Clustering
  • Power BI vs Tableau comparison
  • Git basics: clone, commit, push, pull
  • Version control for notebooks and dashboards
  • Using GitHub for project collaboration