Unlock the Power of Data with Comprehensive Training in Analytics, Machine Learning, and Data Visualization.

A Comprehensive Guide

Comprehensive Training in Machine Learning, Data Visualization, and Predictive Analytics

Learn Cutting-Edge Techniques in Big Data, AI, and Statistical Analysis to Drive Business Decisions

Data Science Tools and Techniques to Solve Complex Problems and Propel Your Career

The skills and knowledge needed to turn data into actionable insights

Data science is a fascinating field that involves extracting insights and knowledge from data. It is also the most demanding profession in the IT industry and also helps industries grow and expand their businesses by extracting valuable data insights from raw information.

Several modern methods are used by data scientists to drive profitability and cater to the need for solutions to real-world problems.

Data science enhances customer experience and optimizes supply chain management in the e-commerce industry.

Data science is the backbone of developing sophisticated ML and AI algorithms for predictive analytics and automation.

Governments utilize data science to improve public services, policy-making, disaster response, and resource allocation.

In healthcare, data science aids in patient care, medical research, disease prevention, and operational efficiency.

Data science optimizes energy production, distribution, and consumption, promoting sustainability.

The finance industry relies on data science for risk management, fraud detection, and investment strategies.

- Basics of SQL Syntax and Commands
- Retrieving Data Using SELECT Statement
- Filtering Data Using WHERE Clause
- Sorting Data Using ORDER BY Clause
- Joins in SQL: INNER JOIN, LEFT JOIN, RIGHT JOIN
- Grouping Data Using GROUP BY Clause
- Subqueries and Common Table Expressions (CTEs)
- Advanced SQL Concepts: Window Functions

- Overview of Excel Interface and Basic Functionalities
- Importing Data into Excel from Various Sources
- Basic Functions: SUM, AVERAGE, COUNT, etc
- Creating PivotTables and PivotCharts
- Data Visualization Using Charts and Graphs
- Text Functions
- Date Functions, and Data Manipulation

- Array
- Operators
- Variables
- Data types
- Comments
- Conditions
- Function
- Loop
- Object
- Regular Expressions
- File handling
- Pickling, and many more essential concepts
- Algorithm thinking

- Definitions and concepts
- Importance in Data Science
- Types of data: Quantitative vs. Qualitative
- Measures of central tendency: Mean, Median, Mode
- Measures of variability: Range, Variance, Standard Deviation, Interquartile Range
- Data visualization: Histograms, Box plots, Bar charts, Scatter plots

- Basic probability concepts
- Probability axioms and rules
- Conditional probability and Bayes’ theorem
- Independent and dependent events
- Probability mass function (PMF) and probability density function (PDF)
- Text Functions
- Date Functions, and Data Manipulation

- Introduction to Machine Learning
- Overview
- Types of Machine Learning
- Linear Regression
- Logistic Regression
- K-Nearest Neighbors (KNN)
- Decision Trees
- Model Evaluation Metrics
- Support Vector Machines (SVM)
- Dimensionality Reduction

- Convolutional Neural Networks (CNNs) for image recognition
- Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) for sequence modeling
- Generative Adversarial Networks (GANs)
- Autoencoders
- Perceptrons and multilayer perceptrons

- Overview of Data Visualization
- Types of Data Visualizations
- Tools and Libraries
- Data Exploration with Visualizations
- Handling Missing Values and Outliers
- Data Distribution and Trends

- Geospatial Data Visualization
- Network Visualization
- Interactive Visualization
- Color Theory and Palette Selection
- Layout and Typography
- Feature Analysis and Importance
- Model Evaluation Visualization

SQL

- Basics of SQL Syntax and Commands
- Retrieving Data Using SELECT Statement
- Filtering Data Using WHERE Clause
- Sorting Data Using ORDER BY Clause
- Joins in SQL: INNER JOIN, LEFT JOIN, RIGHT JOIN
- Grouping Data Using GROUP BY Clause
- Subqueries and Common Table Expressions (CTEs)
- Advanced SQL Concepts: Window Functions

Excel

- Overview of Excel Interface and Basic Functionalities
- Importing Data into Excel from Various Sources
- Basic Functions: SUM, AVERAGE, COUNT, etc
- Creating PivotTables and PivotCharts
- Data Visualization Using Charts and Graphs
- Text Functions
- Date Functions, and Data Manipulation

Python

- Array
- Operators
- Variables
- Data types
- Comments
- Conditions
- Function
- Loop
- Object
- Regular Expressions
- File handling
- Pickling, and many more essential concepts
- Algorithm thinking

Descriptive Statistics

- Definitions and concepts
- Importance in Data Science
- Types of data: Quantitative vs. Qualitative
- Measures of central tendency: Mean, Median, Mode
- Measures of variability: Range, Variance, Standard Deviation, Interquartile Range
- Data visualization: Histograms, Box plots, Bar charts, Scatter plots

Probability Theory

- Basic probability concepts
- Probability axioms and rules
- Conditional probability and Bayes’ theorem
- Independent and dependent events
- Probability mass function (PMF) and probability density function (PDF)
- Text Functions
- Date Functions, and Data Manipulation

Machine Learning

- Introduction to Machine Learning
- Overview
- Types of Machine Learning
- Linear Regression
- Logistic Regression
- K-Nearest Neighbors (KNN)
- Decision Trees
- Model Evaluation Metrics
- Support Vector Machines (SVM)
- Dimensionality Reduction

Deep Learning

- Convolutional Neural Networks (CNNs) for image recognition
- Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) for sequence modeling
- Generative Adversarial Networks (GANs)
- Autoencoders
- Perceptrons and multilayer perceptrons

Data Visualization

- Overview of Data Visualization
- Types of Data Visualizations
- Tools and Libraries
- Data Exploration with Visualizations
- Handling Missing Values and Outliers
- Data Distribution and Trends

Advanced Visualization

- Geospatial Data Visualization
- Network Visualization
- Interactive Visualization
- Color Theory and Palette Selection
- Layout and Typography
- Feature Analysis and Importance
- Model Evaluation Visualization

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The course starts from 3 month to 6 months.

No prior experience is required, though basic programming knowledge is beneficial.

Yes, a certificate of completion will be awarded upon successful completion of the course.