Vista Academy Data Analytics Program Fees

    Duration: 12 Months

    Mode of Instruction : Classroom Training Only

    data analytics program breakdown

    Fees: Rs. 55,000

    Data Analytics Certification in Dehradun

    Course Structure

    Month 1-3: Foundations of Data Analytics

    1. Data Preparation and Analytics Overview

      • Understanding data analytics processes
      • Recognizing data types and sources
      • Data cleaning and transformation techniques

    2. Excel for Data Analysis

      • Week 1: Introduction to Excel
      • Week 2: Essential Formulas and Functions
      • Week 3: Data Cleaning and Transformation
      • Week 4: Data Analysis with PivotTables
      • Week 5: Advanced Excel Techniques
      • Week 6: Data Visualization with Excel
      • Project: Analyze a dataset using Excel and present findings.

    3. SQL for Data Analytics

      • Week 7: Introduction to SQL and Relational Databases
      • Week 8: Retrieving Data with SELECT Statements
      • Week 9: Joining Data from Multiple Tables
      • Week 10: Aggregating Data
      • Week 11: Advanced Querying Techniques
      • Week 12: Hands-on SQL Project
      • Project: Create a database and perform various queries to extract insights.

    Month 4-6: Advanced Data Analytics Techniques

    1. Power BI for Data Analytics

      • Week 13: Introduction to Power BI
      • Week 14: Data Connection and Transformation
      • Week 15: Data Modeling in Power BI
      • Week 16: Data Visualization Techniques
      • Week 17: Advanced Data Transformations
      • Week 18: Power BI Project
      • Project: Develop an interactive dashboard for business insights.

    2. Python for Data Analytics

      • Week 19: Introduction to Python for Data Analysis
      • Week 20: Data Manipulation with Pandas
      • Week 21: Exploratory Data Analysis
      • Week 22: Data Visualization with Matplotlib and Seaborn
      • Week 23: Statistical Analysis with Python
      • Week 24: Python Project
      • Project: Conduct a comprehensive analysis of a dataset using Python and create visualizations.

    Month 7-9: Introduction to Machine Learning

    1. Machine Learning Fundamentals

      • Week 25: Introduction to Machine Learning Concepts
      • Week 26: Supervised vs. Unsupervised Learning
      • Week 27: Data Preprocessing Techniques
      • Week 28: Regression Algorithms
      • Week 29: Classification Algorithms
      • Week 30: Machine Learning Project
      • Project: Build and evaluate a predictive model using machine learning techniques.

    2. JIRA Software Training

      • Week 31: Introduction to JIRA and Agile Methodology
      • Week 32: Managing Projects and Tasks in JIRA
      • Week 33: Collaborating in Teams Using JIRA
      • Project: Use JIRA to manage a team project throughout the course.

    3. GitHub Training

      • Week 34: Introduction to Git and GitHub
      • Week 35: Version Control Best Practices
      • Week 36: Collaborative Development on GitHub
      • Project: Contribute to an open-source project or create a personal project repository on GitHub.

    Month 10-12: Capstone Project and Portfolio Development

    1. Capstone Project

      • Month 10: Proposal Development
      • Month 11: Project Execution and Data Collection
      • Month 12: Final Project Presentation
      • Capstone Project: Students will select a real-world problem, apply their analytical skills, and present their findings using various tools and techniques learned throughout the course.

    2. Portfolio Development

      • Month 12: Building Your Portfolio
      • Resume Building Workshop
      • Interview Preparation: Mock Interviews and Technical Skills Review
      • Final Portfolio Presentation: Showcase projects completed during the course to potential employers.

    Course Curriculum:

    Vista Academy Data Science Program Fees

    Duration: 15 Months

    Welcome to Vistashiksha Solutions Pvt Ltd.

    Fees: Rs. 75,000

    Mode of Instruction : Classroom Training Only

    15-Month Data Science Syllabus

    Month 1-3: Foundations of Data Science

    Course Objective: Introduce students to the fundamental concepts of data science, data types, and essential tools.

    • Month 1: Introduction to Data Science

      • What is Data Science?
      • Overview of the Data Science Lifecycle
      • Key Terminologies and Concepts
      • Data Collection Methods

    • Month 2: Data Manipulation with Python

      • Introduction to Python for Data Science
      • Data Types and Structures
      • Libraries: NumPy and Pandas
      • Data Cleaning and Preprocessing

    • Month 3: Data Visualization

      • Importance of Data Visualization
      • Introduction to Matplotlib and Seaborn
      • Basic Plotting Techniques
      • Advanced Visualization Techniques

    Month 4-5: Statistical Foundations

    Course Objective: Provide students with a strong statistical background necessary for data analysis.

    • Month 4: Descriptive Statistics

      • Measures of Central Tendency
      • Measures of Dispersion
      • Data Distribution and Visualization

    • Month 5: Inferential Statistics

      • Probability Concepts
      • Hypothesis Testing
      • Confidence Intervals
      • Regression Analysis

    Month 6-7: Data Engineering

    Course Objective: Introduce students to data storage, databases, and data engineering concepts.

    • Month 6: SQL and Relational Databases

      • Introduction to SQL
      • Basic Queries and Joins
      • Aggregation and Grouping Data
      • Advanced SQL Functions

    • Month 7: Data Warehousing and ETL Processes

      • Introduction to Data Warehousing Concepts
      • ETL (Extract, Transform, Load) Processes
      • Tools: Apache Airflow, Talend

    Month 8-9: Advanced Data Analysis

    Course Objective: Deepen students’ understanding of advanced data analysis techniques.

    • Month 8: Machine Learning Fundamentals

      • Introduction to Machine Learning Concepts
      • Types of Machine Learning: Supervised vs. Unsupervised
      • Key Algorithms: Linear Regression, Decision Trees

    • Month 9: Unsupervised Learning Techniques

      • Clustering Techniques (K-means, Hierarchical Clustering)
      • Principal Component Analysis (PCA)
      • Anomaly Detection

    Month 10-11: Advanced Machine Learning

    Course Objective: Equip students with advanced machine learning algorithms and techniques.

    • Month 10: Supervised Learning Algorithms

      • Advanced Regression Techniques (Ridge, Lasso)
      • Ensemble Learning (Random Forest, Boosting)
      • Support Vector Machines (SVM)

    • Month 11: Neural Networks and Deep Learning

      • Introduction to Neural Networks
      • Convolutional Neural Networks (CNNs) for Image Data
      • Recurrent Neural Networks (RNNs) for Time-Series Data

    Month 12: Data Science Tools and Technologies

    Course Objective: Familiarize students with industry-standard tools used in data science.

    • Data Visualization Tools:
      • Tableau: Building Dashboards and Reports
      • Power BI: Interactive Data Visualization
    • Version Control and Collaboration:
      • Introduction to Git and GitHub
      • Best Practices for Version Control
    • Big Data Technologies:
      • Introduction to Hadoop and Spark

    Month 13: Projects and Portfolio Development

    Course Objective: Provide students with hands-on experience through projects to build their portfolios.

    • Capstone Project Selection:

      • Choose a project topic relevant to data science
      • Define project goals and objectives

    • Project Development:

      • Data Collection and Cleaning
      • Exploratory Data Analysis
      • Model Building and Evaluation
      • Final Presentation and Reporting

    Month 14: Industry Applications of Data Science

    Course Objective: Explore real-world applications of data science in various industries.

    • Industry Use Cases:
      • Data Science in Healthcare
      • Data Science in Finance
      • Data Science in Marketing
      • Data Science in E-Commerce

    Month 15: Career Preparation and Placement

    Course Objective: Prepare students for job placements in the data science field.

    • Resume Building and LinkedIn Optimization
    • Interview Preparation:
      • Technical Interview Techniques
      • Behavioral Interview Preparation
    • Networking and Industry Connections
    • Job Placement Assistance:
      • Connecting with Industry Recruiters
      • Mock Interviews
    Empower Uttarakhand's youth for a data-driven Job!"