DATA ANALYST ONLINE TRAINING 

Batch type:  Weekdays/Weekends

Mode of Training:  Classroom/Online/Corporate Training

Highly Experienced Certified Trainer with 10+ yrs Exp. in Industry

Realtime Projects, Scenarios & Assignments

What will you get along with the training?
1) course completion certificate.
2) 1 live project.
4) Resume building & Interview preparation.
5) Interview call from Tie-up companies until you successfully get placed.
6) Hands-on training with real-time scenarios.
7) Certification Based training with study material.

A Data Analyst curriculum focusing following topics:

1.  Introduction to Data Analysis

Overview of data analysis and its importance

Role and responsibilities of a data analyst

Understanding the data analysis process

2.  Data Exploration and Visualization

Exploring and summarizing data using descriptive statistics

Creating meaningful visualizations with charts and graphs

Identifying patterns and trends in data

3.  Data Cleaning and Preparation

Handling missing data and outliers

Dealing with data inconsistencies and errors

Data transformation and formatting techniques

4.  Data Manipulation and Querying

Working with data using SQL (Structured Query Language)

Filtering, sorting, and aggregating data

Joining multiple tables and creating complex queries

5.  Exploratory Data Analysis (EDA)

Conducting EDA techniques (histograms, scatter plots, etc.)

Correlation analysis and identifying relationships

Hypothesis testing and statistical analysis

6.  Data Visualization with Tools

Introduction to data visualization tools (Tableau, Power BI, etc.)

Creating interactive dashboards and reports

Customizing visualizations for effective communication

7.  Data Modeling and Predictive Analytics

Introduction to predictive analytics concepts

Building predictive models (regression, classification, etc.)

Model evaluation and performance metrics

8.  Time Series Analysis

Understanding time series data and its characteristics

Decomposition and trend analysis

Forecasting techniques (moving average, exponential smoothing, etc.)

9.  Data Mining and Machine Learning

Introduction to data mining techniques

Applying machine learning algorithms (clustering, decision trees, etc.)

Feature selection and model evaluation

10. Data Presentation and Communication

Presenting data insights effectively

Creating clear and compelling data-driven stories

Communicating findings to stakeholders

11. Ethical Considerations in Data Analysis

     Understanding ethical implications in data analysis

     Privacy, data security, and compliance

     Ensuring data integrity and responsible data practices

12.Real-World Projects and Case Studies

     Applying data analysis skills to real-world scenarios

     Completing hands-on projects and exercises

     Analyzing and interpreting real data sets

     Presenting findings and insights