Advance Professional Courses

Data Analysis Course

You’ll gain hands-on experience with tools like Excel, SQL, Tableau, Power BI, and Python

Based on 99 Reviews

Data Analysis Course

Data Analytics Course Overview

Want to excel in data-driven decision-making? Our Data Analysis Courses are designed to help you master data visualization, statistical analysis, and predictive modeling. Learn essential tools like Excel, SQL, Python, and Tableau to analyze and interpret complex data efficiently. Gain hands-on experience with real-world projects and case studies. Whether you’re a beginner or a professional, our expert-led training ensures you develop industry-relevant skills. Enroll now in our Data Analysis Courses and take your data analytics expertise to the next level!

Why Choose EduNova for Data Analysis Courses?

EduNova’s Data Analysis Courses provide in-depth training on data visualization, statistical techniques, and data-driven decision-making. Our expert-led curriculum includes hands-on projects, real-world case studies, and industry-recognized tools like Excel, SQL, and Python. Whether you’re new to data analytics or looking to enhance your skills, EduNova ensures comprehensive learning to boost your career. With flexible learning options and certification, we help you gain practical expertise. Join EduNova today and unlock new career opportunities in data analysis!

Key Features
Globally Recognised Certification
Dedicated Placement Cell & HR Support
Live Online and Offline Classes
Dedicated doubts Session
Lifetime Career Guidance & Job Change Support
Highly Skilled Trainers
Course Topics You will Learn
Overview of Data Analytics
  • Importance and applications in business, healthcare, finance, and more
  • Types of Data Analytics: Descriptive, Diagnostic, Predictive, Prescriptive
Data Analytics Process
  • Data Collection, Cleaning, and Preprocessing
  • Exploratory Data Analysis (EDA)
  • Data Visualization
  • Reporting and Decision Making
Data Types and Structures
  • Types of Data: Structured, Semi-structured, Unstructured
  • Data Formats: CSV, JSON, XML
  • Data Structures in Python: Lists, Tuples, Dictionaries, Pandas DataFrame
Data Collection Methods
  • Web Scraping and APIs
  • Data from Databases (SQL)
Data Cleaning and Preprocessing
  • Missing Data Handling (Imputation, Removal)
  • Outlier Detection and Treatment
  • Data Normalization and Standardization
  • Data Transformation Techniques (e.g., encoding categorical variables)
Python for Data Analytics
  • Introduction to Python Libraries: Pandas, NumPy, Matplotlib, Seaborn
  • Data Manipulation with Pandas: Filtering, Grouping, Aggregating Data
  • Data Aggregation and Pivot Tables
NumPy for Data Handling
  • Arrays and Matrices in NumPy
  • Mathematical and Statistical Operations
  • Array Indexing and Slicing
Matplotlib and Seaborn for Data Visualization
  • Creating Basic Plots (Line, Bar, Histogram, Boxplot)
  • Customizing Visualizations (Titles, Labels, Legends)
  • Advanced Visualizations (Heatmaps, Pair Plots)
Data Visualization Tools for Data Science

Tableau

  • Introduction to Tableau Interface
  • Connecting Tableau to different data sources
  • Creating visualizations: Bar charts, Line charts, Scatter plots, Pie charts
  • Building Dashboards
  • Advanced Visualizations: Heatmaps, Tree Maps, Bullet Charts
  • Interactive features: Filters, Parameters, Tooltips, and Dashboard Actions
  • Publishing and Sharing Tableau Dashboards

Power BI

  • Introduction to Power BI Interface
  • Importing and Transforming Data with Power Query
  • Data Modeling in Power BI: Relationships, DAX Functions
  • Creating Visualizations: Bar charts, Line charts, and Maps
  • Building Reports and Dashboards
  • Interactive Features: Slicers, Drill-through, and Tooltips
  • Power BI Service: Publishing, Sharing, and Collaborating
Introduction to Predictive Analytics
  • Types of Predictive Models
  • Use Cases: Customer Churn Prediction, Sales Forecasting
Supervised Learning Algorithms
  • Linear Regression for Predictive Analysis
  • Decision Trees and Random Forests
  • Support Vector Machines (SVM)
  • k-Nearest Neighbors (k-NN)
Model Evaluation Techniques
  • Train-Test Split
  • Cross-Validation
  • Model Performance Metrics: Accuracy, Precision

This Course Include

Eligibility Criteria

✅ Anyone with basic computer knowledge can join our courses based on their preferences and career goals.
✅ Suitable for all age groups and backgrounds, including:

  • School Students – Build a strong foundation in coding and technology.
  • Job Seekers & Undergraduates – Gain in-demand skills to enhance employability.
  • Working Professionals & Government Employees – Upskill for career growth and new opportunities.
  • Homemakers – Learn and explore career opportunities in the tech industry.

Premium Course

Register For This Course

    Who will Teach You
    Haninder Kaur
    Haninder Kaur
    Data Analyst

    About Mrs. Haninder Kaur

    Haninder Kaur, an MCA graduate with over 5 years of experience, specializes in Python, Artificial Intelligence (AI), and Machine Learning (ML). She is passionate about leveraging these technologies to develop innovative solutions that solve complex problems. Haninder has contributed to projects involving data analysis, predictive modeling, and automation. Her ability to break down complex concepts into digestible knowledge makes her an excellent mentor for those eager to explore AI and ML. Haninder’s expertise and forward-thinking approach position her as a key player in the rapidly evolving tech landscape.

    Based on 99 Reviews

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    What skills are in-demand in the job market?

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    Digital marketing
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    Data Analytics
    Quantum computing/ Cloud computing
    Android App development
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    What skills are in-demand in the job market?

    Python
    Data science
    Artificial Intelligence
    Digital marketing
    Cyber Security
    Data Analytics
    Quantum computing/ Cloud computing
    Android App development
    Web development