DATA ANALYTICS – Excel - PowerBI – Tableau - SQL & Python Training in Bangalore
ACETECK provides best DATA ANALYTICS – Excel - PowerBI – Tableau - SQL & Python Training with 100% Job Placement assistance. Get trained from industry experts & Start your IT career.
Unlock the power of your data with cutting-edge analytics! Our training equips you with the skills to transform raw data into actionable insights, driving smarter decisions and fostering innovation. Transform complex data into clear insights and actionable strategies to drive success and innovation in your field.
1. Introduction to Data Analysis
- What is Data Analysis?
– Importance of Data in Decision-Making
– Types of Data (Structured vs. Unstructured)
– Role of a Data Analyst
2. Mathematics and Statistics for Data Analysis
– Descriptive Statistics (Mean, Median, Mode, Standard Deviation, Variance)
– Probability Theory
– Inferential Statistics (Hypothesis Testing, Confidence Intervals, p-value)
– Correlation vs. Causation
– Linear Algebra Basics
– Introduction to Data Distributions
3. Excel for Data Analysis
– Data Cleaning and Transformation
– Advanced Functions (VLOOKUP, HLOOKUP, INDEX, MATCH)
– Pivot Tables and Charts
– Data Visualization in Excel
4. Database and SQL
- Introduction to Databases
– SQL Basics (SELECT, WHERE, JOINs, GROUP BY, ORDER BY)
– Data Aggregation and Filtering
– Subqueries and Nested Queries
– SQL for Data Cleaning and Preparation
– Advanced SQL Topics (Window Functions, CTEs, Views)
5. Data Manipulation with Python
– Introduction to Python for Data Analysis
Libraries: Pandas, NumPy
DataFrames and Series
Data Wrangling (Handling Missing Data, Duplicates)
Data Merging and Concatenation
Applying Functions to Data (Lambda, Apply, Map)
6. Data Visualization
Importance of Data Visualization
Excel and Python Visualization
Matplotlib and Seaborn Libraries
Line, Bar, Pie Charts, Histograms, and Scatterplots
Heatmaps and Correlation Plots
Tableau/Power BI (optional)
Creating Dashboards
Visualizing Large Datasets
Interactive Reports
7. Exploratory Data Analysis (EDA)
- Understanding Data Patterns and Distributions
- Identifying Outliers and Anomalies
- Data Transformation Techniques (Log, Scaling)
- Feature Engineering
8. Introduction to Machine Learning (Optional)
- Supervised vs. Unsupervised Learning
Linear and Logistic Regression
Clustering (K-Means, Hierarchical Clustering)
Decision Trees and Random Forests
Model Evaluation (Accuracy, Precision, Recall, F1-Score)
9. Data Cleaning and Preprocessing
- Handling Missing Data
- Data Transformation (Scaling, Normalization)
- Feature Encoding (One-Hot, Label Encoding)
- Handling Outliers and Anomalies
10. Projects and Case Studies
- Real-world Data Analysis Projects
- End-to-End Projects (from Data Collection to Reporting)
- Case Studies on Business Problem Solving with Data
11. Business Intelligence Tools
- Introduction to BI Tools (Power BI, Tableau, Looker)
Data Connection and Integration with BI Tools
Creating Interactive Dashboards
Sharing Reports and Insights with Stakeholders
12. Soft Skills for Data Analysts
- Critical Thinking and Problem-Solving
- Communication Skills (Presenting Data Insights)
Storytelling with Data - Working with Cross-Functional Teams
13. Version Control & Collaboration
- Introduction to Git and GitHub
- Version Control for Code and Projects
- Collaborating with Teams on Data Projects
14. Final Capstone Project
- Choose a domain-specific project (e.g., Finance, Healthcare, E-commerce)
- Collect, Clean, Analyze, and Visualize Data
- Present Insights and Recommendations