The Data Analytics is the process of examining data sets to find trends and draw conclusions about the information they contain. It involves analyzing raw data, collecting and cleaning it, and transforming it into actionable insights. These insights can be used to support decision-making, improve business operations, and gain a competitive advantage.
Key Features of Course Divine:
Career Opportunities after Data analytics:
Essential Skils you will Develop Data analytics:
Tools Covered:
Syllabus:
Module 1: Introduction to Data Analytics What is Data Analytics? Types of Analytics: Descriptive, Diagnostic, Predictive, Prescriptive Data Lifecycle and Analytics Pipeline
Applications across Industries Roles: Data Analyst vs Data Scientist vs Data Engineer.
Module 2: Excel for Data Analysis Excel Basics: Formulas, Functions, and Formatting Lookup Functions (VLOOKUP, HLOOKUP, INDEX-MATCH) Pivot Tables and Charts Data Cleaning in Excel Dashboards using Excel.
Module 3: Statistics and Probability for Data Analytics Mean, Median, Mode, Variance, Standard Deviation Probability Distributions Hypothesis Testing (Z-test, T-test, Chi-square) Correlation vs Causation Inferential vs Descriptive Statistics.
Module 4: SQL for Data Analytics Introduction to Databases SELECT, WHERE, GROUP BY, HAVING, ORDER BY JOINs: INNER, LEFT, RIGHT, FULL Subqueries and CTEs Aggregation and Window Functions.
Module 5: Data Visualization Tools (Tableau / Power BI) Introduction to BI Tools
Data Import and Transformation Creating Dashboards and Reports Charts: Bar, Line, Pie, Maps, TreeMaps Storytelling with Data.
Module 6: Python for Data Analysis Python Basics: Variables, Lists, Dictionaries, Loops, Functions Libraries: NumPy, Pandas Data Cleaning and Manipulation with Pandas Handling Missing Data and Outliers File Handling CSV, Excel, JSON.
Module 7: Data Wrangling & EDA Exploratory Data Analysis Data Collection and Inspection Removing Duplicates and Nulls Feature Engineering & Transformation
Univariate & Bivariate Analysis Visualization using Seaborn & Matplotlib.
Module 8: Introduction to Machine Learning Supervised vs Unsupervised Learning
Regression and Classification Basics Model Building using Scikit-Learn Train-Test Split, Cross Validation Model Evaluation Metrics Accuracy, F1, ROC.
Module 9: Data Analytics in Real Business Use Cases Sales Analysis Customer Retention Supply Chain Optimization Marketing Campaign Effectiveness Finance Forecasting & Reporting.
Module 10: Capstone Project & Career Preparation Real-World Project (from dataset to dashboard) Final Report Submission & Presentation Resume Building & Portfolio Setup Interview Questions for Data Analyst Role Freelance & Job Market Guidance.
Industry Projects:
Who is this program for?
How To Apply:
Mobile: 9100348679
Email: coursedivine@gmail.com
You cannot copy content of this page
