Course

Data Science & Analytics

Data Science and Analytics professionals use statistical methods, algorithms, and tools to analyse large datasets, uncover trends, and help organisations make data-driven decisions.

XBYTE TERA SOLUTIONS PRIVATE LIMITED Join us for an online and offline course and achieve your dreams.

6 month training + 6 month internship with stipend

Why to choose our course?

  • Top Industry Experts as Trainers
  • Intensive Hands-on Training
  • Flexible Timing for your convenience
  • Interview Preparation support
  • Job Assistance after course completion
  • Seats are limited. Save the date and don't miss out!
  • Hostel facility available
    • Register Now

Syllabus

Introduction to Data Analytics
  • What is Data Analytics?
  • Importance of Data Analytics in Business
  • Types of Data Analytics (Descriptive, Diagnostic, Predictive, Prescriptive)
  • Data Analytics vs. Data Science vs. Business Intelligence
  • Real-world Applications of Data Analytics
Statistics and Probability
  • Descriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation)
  • Inferential Statistics (Hypothesis Testing, Confidence Intervals)
  • Probability Theory (Basic Probability, Bayes’ Theorem)
  • Correlation and Regression Analysis
  • A/B Testing
Data Collection and Cleaning
  • Types of Data (Structured vs. Unstructured)
  • Data Sources (Databases, APIs, Web Scraping, Excel, CSV)
  • Data Cleaning Techniques (Handling Missing Data, Removing Duplicates, Data Transformation)
  • Data Wrangling with Python & Pandas
Data Visualisation
  • Importance of Data Visualization
  • Tools for Visualization
    • Excel (Charts, Pivot Tables)
    • Power BI / Tableau
    • Python (Matplotlib, Seaborn, Plotly)
  • Creating Dashboards and Reports
SQL for Data Analysis
  • Introduction to Databases
  • SQL Basics (SELECT, INSERT, UPDATE, DELETE)
  • Joins and Subqueries
  • Aggregate Functions (SUM, COUNT, AVG, GROUP BY, HAVING)
  • Window Functions and CTEs
  • Indexing and Query Optimization
Python for Data Analysis
  • Python Basics (Variables, Data Types, Loops, Functions)
  • Working with Pandas & NumPy
  • Data Manipulation with Pandas
  • Exploratory Data Analysis (EDA)
  • Automating Data Tasks with Python
Excel for Data Analysis
  • Functions and Formulas (VLOOKUP, HLOOKUP, INDEX-MATCH)
  • Pivot Tables and Charts
  • Data Validation and Conditional Formatting
  • Macros and VBA (Basic Automation)
Business Intelligence Tools
  • Introduction to BI Tools (Power BI, Tableau)
  • Connecting to Databases and APIs
  • Creating Interactive Dashboards
  • Data Storytelling Techniques
Machine Learning Basics (Optional)
  • Introduction to Machine Learning
  • Supervised vs. Unsupervised Learning
  • Basic ML Models (Linear Regression, Decision Trees)
  • Model Evaluation Metrics
Case Studies and Real-world Projects
  • Retail Sales Analysis
  • Customer Segmentation
  • Financial Data Analysis
  • Healthcare Data Insights

Tools

PYTHON
PANDAS
NUMPY
Power BI
Seaborn
Matplotlib

Benefits of Data Science & Analytics

  • Business Decision Making Enables businesses to make informed decisions using data-driven insights.
  • Job Market Growth The demand for data science professionals is rapidly increasing across the globe.
  • Opportunities Across Industries Data science is applied in various fields such as Finance, Healthcare, E-commerce, and Marketing

Job Categories in Data Science & Analytics

  • Data Analyst
  • Data Scientist
  • Business Analyst
  • Machine Learning Engineer
  • Data Engineer
  • AI Engineer
  • Quantitative Analyst (Quant)
  • Research Scientist – AI/ML