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Data Science, Data Engineering & Data Analytics Training

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Data Science Training

Data Science

Python, statistics, machine learning, AI and practical projects.

Data Engineering Training

Data Engineering

ETL, SQL, data warehousing, cloud data platforms and pipelines.

Data Analytics Training

Data Analytics

SQL, Excel, BI dashboards, visualization and business insights.

SoftApp Technologies

Data Science • Data Engineering • Data Analytics

Beginner to industry-oriented level with practical, project-based learning.

  • Data Science Overview & Industry Applications
  • Python Fundamentals for Data Science
  • Variables, Data Types, Operators & Control Flow
  • Functions, Modules & Packages
  • Lists, Tuples, Sets & Dictionaries
  • File Handling & Exception Handling
  • Object-Oriented Programming Basics
  • NumPy Fundamentals
  • Pandas Series & DataFrames
  • Data Import / Export — CSV, Excel, JSON

  • Descriptive Statistics
  • Mean, Median, Mode & Percentiles
  • Variance & Standard Deviation
  • Probability Fundamentals
  • Probability Distributions
  • Correlation & Covariance
  • Hypothesis Testing
  • Confidence Intervals
  • Sampling Concepts
  • Linear Algebra Basics for Machine Learning

  • Data Quality & Data Preparation
  • Missing Values
  • Duplicate Records
  • Outlier Detection & Treatment
  • Data Type Conversion
  • Feature Inspection
  • Exploratory Data Analysis (EDA)
  • GroupBy, Aggregation & Pivot Analysis
  • Data Visualization with Matplotlib
  • Data Visualization with Seaborn
  • Business Questions to Data Insights

  • Machine Learning Fundamentals
  • Supervised vs Unsupervised Learning
  • Train / Validation / Test Split
  • Feature Engineering
  • Feature Scaling & Encoding
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forest
  • Support Vector Machines
  • K-Nearest Neighbors
  • Naive Bayes
  • K-Means Clustering
  • Dimensionality Reduction Basics
  • Model Evaluation & Cross-Validation
  • Classification Metrics — Accuracy, Precision, Recall, F1
  • Regression Metrics — MAE, MSE, RMSE, R²
  • Hyperparameter Tuning
  • Scikit-learn Workflow

  • Ensemble Learning Concepts
  • Gradient Boosting Concepts
  • Feature Selection
  • Model Interpretability Basics
  • Time-Series Analysis Fundamentals
  • Forecasting Concepts
  • Natural Language Processing Fundamentals
  • Text Preprocessing
  • Sentiment Analysis
  • Introduction to Deep Learning
  • Neural Network Fundamentals
  • AI / Generative AI Concepts
  • Using AI Assistants in Data Analysis Workflows

  • Data Engineering Overview
  • OLTP vs OLAP
  • Relational Database Fundamentals
  • SQL Fundamentals
  • SELECT, WHERE, ORDER BY
  • GROUP BY & HAVING
  • Joins
  • Subqueries & CTEs
  • Window Functions
  • Views
  • Indexes & Query Optimization Basics
  • Transactions
  • Stored Procedures & Functions
  • Data Modeling Fundamentals
  • Normalization & Denormalization

  • ETL vs ELT
  • Data Ingestion
  • Batch Processing
  • Incremental Loads
  • Full Loads
  • Data Transformation
  • Data Validation & Quality Checks
  • Pipeline Scheduling
  • Error Handling & Retry Strategies
  • Logging & Monitoring Pipelines
  • Workflow Orchestration Concepts
  • Airflow Fundamentals
  • DAGs, Tasks & Operators
  • Pipeline Dependencies

  • Python for Data Engineering
  • Working with APIs
  • JSON / CSV / Parquet Data
  • Database Connectivity
  • Building Data Ingestion Scripts
  • Apache Spark Fundamentals
  • Spark Architecture
  • RDD Concepts
  • DataFrames & Spark SQL
  • Transformations & Actions
  • Partitioning
  • PySpark Data Processing
  • Big Data Processing Concepts

  • Data Warehouse Concepts
  • Star Schema
  • Snowflake Schema
  • Fact & Dimension Tables
  • Data Lake Concepts
  • Data Lakehouse Concepts
  • Cloud Data Engineering Fundamentals
  • AWS S3 for Data Lakes
  • AWS Glue Concepts
  • AWS Athena Concepts
  • Amazon Redshift Concepts
  • Cloud ETL Architecture
  • Data Catalog & Metadata Concepts
  • Security, IAM & Data Access Controls

  • Data Pipeline Architecture
  • Scalable Data Processing
  • Partitioning & Performance
  • Data Quality Frameworks
  • Pipeline Monitoring
  • Data Lineage Concepts
  • Data Governance Basics
  • Secrets & Configuration Management
  • CI/CD for Data Pipelines
  • Infrastructure as Code Concepts
  • Production Troubleshooting
  • Backup & Recovery Concepts

  • Data Analytics Fundamentals
  • Business Problem Definition
  • Excel for Data Analysis
  • Formulas & Functions
  • Lookup Functions
  • Pivot Tables & Pivot Charts
  • Data Cleaning in Excel
  • SQL for Data Analytics
  • Aggregations & Grouping
  • Joins for Analytics
  • CTEs & Window Functions
  • Analytical Query Design

  • Data Visualization Principles
  • Dashboard Design
  • Power BI Fundamentals
  • Power Query
  • Data Modeling in Power BI
  • DAX Fundamentals
  • Measures & Calculated Columns
  • Interactive Reports
  • KPIs & Business Metrics
  • Drill-Down & Filters
  • Dashboard Publishing Concepts
  • Business Intelligence Workflow

  • Descriptive Analytics
  • Diagnostic Analytics
  • Predictive Analytics Concepts
  • Prescriptive Analytics Concepts
  • Trend Analysis
  • Customer Analytics
  • Sales Analytics
  • Marketing Analytics
  • Financial Analytics
  • Operational Analytics
  • Data Storytelling
  • Executive Dashboards
  • Communicating Insights to Stakeholders

  • AI for Data Analysis
  • Natural-Language Data Questions
  • AI-Assisted SQL Generation
  • AI-Assisted Data Cleaning
  • AI-Assisted Insight Generation
  • Automated Reporting Concepts
  • Responsible Use of AI in Analytics
  • Human Review and Validation of AI-Generated Insights

  • Business Requirement & Use-Case Definition
  • Data Source Identification
  • Data Ingestion
  • Data Cleaning & Transformation
  • Data Warehouse / Lake Design
  • ETL / ELT Pipeline Development
  • Analytics Data Model
  • Machine Learning / Predictive Analysis Component
  • Power BI / Business Dashboard
  • Pipeline Monitoring & Data Quality
  • Documentation & Presentation
  • End-to-End Business Case Study