Mastering in
Data Science

Fees:starting from
โน 25,500/-
- Course Duration: 6 Months
- Weekly Classes: 2 days a week
- Class Duration: 1 โ 2 Hrs.
- Mode of Class: Online โ Offline โ Hybrid
Course Description:
Unlock Your Data Science Potential with Ascent InfoTech's Certificate Course in Kolkata!
Looking to embark on a rewarding journey into the world of Data Science right here in Kolkata? Ascent InfoTech Computer Training Institute proudly presents our comprehensive ‘Certificate Course for Data Science’, where we deliver the finest Data Science education to help you master the skills and techniques that matter most in this thriving field
Course Highlights / What you will learn:
Python-Powered Learning:
Immerse yourself in the versatile world of Python programming, honing your skills from the basics to advanced concepts tailored for data science.
NumPy and Pandas Prowess:
Become a data manipulation virtuoso with in-depth training in NumPy and Pandas, the cornerstones of data analysis.
SQL for Data Retrieval:
Master SQL to seamlessly extract, manage, and manipulate data from databases, a must-have skill in the data scientist’s toolkit.
Advanced Excel Expertise:
Unleash the full potential of Excel through advanced functions, PivotTables, and stunning data visualizations.
Real-World Projects:
Translate your knowledge into practical solutions by working on real data science projects, building a portfolio that speaks volumes.
Why Choose Ascent InfoTech for Data Science Course?
Seasoned Instructors:
ย Learn from industry veterans with extensive experience in Data Science.
Hands-On Immersion:
Develop practical skills through hands-on exercises and projects.
Career Elevation:
Receive invaluable job placement assistance and interview preparation.
Cutting-Edge Environment:
Train in a state-of-the-art facility, embracing the latest technology.
Whether you’re a novice eager to explore or a professional aiming to upskill, our course serves as your gateway to a fulfilling Data Science career.
Elevate your prospects with Ascent InfoTech Computer Training Institute in Kolkata, where Data Science excellence converges with real-world application. Don’t miss this opportunityโenroll today to take your first step towards Data Science Training Course in Kolkata with Ascent InfoTech.”
Here's a comprehensive and exhaustive course
content outline for "Excel for Data Science":
Introduction to Data Science with Excel
- Understanding the Role of Excel in Data Science
- Data Science Workflow and Excel Integration
- Setting Up Excel for Data Analysis
Excel Basics:
- Introduction to Excel Interface
- Navigating Worksheets and Workbooks
- Entering and Formatting Data
- Basic Excel Functions and Formulas
Data Import and Export
- Importing Data from External Sources
- Cleaning and Preprocessing Data
- Exporting Data to Different Formats
Data Manipulation and Transformation
- Sorting and Filtering Data
- Data Validation and Data Cleaning
- Data Transformation with Excel Functions
- PivotTables for Data Aggregation
Data Visualization in Excel
- Creating Basic Charts (Bar, Line, Pie)
- Customizing Chart Elements
- Advanced Chart Types (Scatter, Heatmap)
Data Analysis Tools in Excel
- Introduction to Excel’s Data Analysis Add-in
- Descriptive Statistics and Data Summaries
- Regression Analysis and Forecasting
- Solving Complex Problems with Solver
Excel Macros for Automation
- Introduction to Excel Macros and VBA
- Recording and Editing Macros 8.3. Automating Repetitive Tasks
- User-Defined Functions (UDFs)
Power Query and Power Pivot
- Data Transformation with Power Query
- Data Modeling with Power Pivot
- Creating Data Relationships
- Advanced-Data Analysis with DAX Functions
Power Query and Power Pivot
- Data Transformation with Power Query
- Data Modeling with Power Pivot
- Creating Data Relationships
- Advanced-Data Analysis with DAX Functions
Importing and Visualizing External Data
- Importing Data from SQL Databases
- Connecting to Web Data Sources
- Real-time Data Updates and Dashboards
- Data Visualization with Power BI
Case Studies and Projects
- Exploratory Data Analysis (EDA) in Excel
- Predictive Modeling and Regression Analysis
- Financial Analysis and Budgeting
Data Science Ethics and Best Practices
- Data Privacy and Ethical Considerations
- Data Security in Excel
- Collaborative Data Science with Excel
- Documentation and Reporting
Career Development in Data Science
- Building a Data Science Portfolio
- Resume Building and LinkedIn Optimization
- Interview Preparation and Mock Interviews
- Job Opportunities in Data Science
This comprehensive course equips learners with the knowledge and skills needed to perform data analysis and visualization using Microsoft Excel, making it suitable for individuals looking to excel in the field of Data Science with a strong foundation in Excel-based data manipulation and analytics.
Here is a comprehensive and exhaustive course
content for a
Python for Data Science course:
Introduction to Data Science and Python
- Understanding Data Science and its Importance
- Role of Python in Data Science
- Setting up Python Environment (Anaconda, Jupyter Notebook)
Python Basics
- Python Syntax and Data Types
- Variables, Operators, and Expressions
- Control Structures (if-else, loops)
- Functions and Modules
NumPy - Numerical Python
- Introduction to NumPy
- Creating and Manipulating Arrays\Array Operations and Broadcasting.
- Indexing and Slicing
- Working with Random Data
Pandas - Data Manipulation
- Introduction to Pandas
- Series and DataFrames
- Data Cleaning and Preprocessing
- Data Exploration and Visualization
- Handling Missing Data
Data Visualization with Matplotlib and Seaborn
- Introduction to Matplotlib and Seaborn
- Creating Line Plots, Scatter Plots, and Bar Charts
- Customizing Plots and Adding Labels
- Seaborn for Statistical Visualization
Data Analysis and Statistics
- Descriptive Statistics
- Inferential Statistics
- Hypothesis Testing
- Correlation and Regression Analysis
- ANOVA and Chi-Square Tests
Machine Learning Basics
- Introduction to Machine Learning
- Supervised vs. Unsupervised Learning
- Model Training and Evaluation
- Classification and Regression Algorithms
Scikit-Learn - Machine Learning with Python
- Overview of Scikit-Learn
- Data Preprocessing with Scikit-Learn
- Building and Evaluating Machine Learning Models
- Model Selection and Hyperparameter Tuning
Data Mining and Text Analytics
- Text Preprocessing and Tokenization
- Text Classification and Sentiment Analysis
- Association Rule Mining
- Clustering and Dimensionality Reduction
Time Series Analysis
- Introduction to Time Series Data
- Time Series Decomposition
- Forecasting Techniques
- Handling Seasonality and Trends
Big Data and Spark
- Introduction to Big Data
- Apache Spark for Data Processing
- Distributed Data Analysis with PySpark
Deep Learning with TensorFlow and Keras
- Introduction to Deep Learning
- TensorFlow and Keras Overview
- Building and Training Deep Neural Networks
- Image Recognition and Natural Language Processing (NLP)
Data Science Projects
- Capstone Project: Real-World Data Analysis
- Mini-Projects: Exploratory Data Analysis, Machine Learning, and Visualization
Deployment and Model Management
- Model Deployment Strategies
- Building APIs with Flask
- Model Versioning and Management
Ethical and Legal Considerations
- Data Privacy and Ethics in Data Science
- Regulatory Compliance (e.g., GDPR)
- Intellectual Property and Data Ownership
This comprehensive course equips learners with the knowledge and skills needed to excel in the field of Data Science using Python. It covers foundational concepts, data manipulation, visualization, statistical analysis, machine learning, deep learning, and practical projects to build a strong foundation in this exciting and rapidly growing field.
Prerequisites:
- Students should have proficiency in any Object-oriented programming.