Data Analytics Internship Program

Learn fundamentals of data analysis concepts, statistical methods and tools, deepen proficiency in Python languages, Data Wrangling and Cleaning, Data Visualization, Exploratory Data Analysis (EDA),Machine Learning Fundamentals, statistical analysis, Database Management, API Integration, Web Scraping, Cloud Computing for Data Analysis, Model Deployment.

CERTIFICATION PROGRAM!


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1000+

Students Trained

50+

Hours of Lectures

Google Ratings:

4.8

Duration

1 to 2 Months

Hybrid Mode

Online + Offline

Micro Batches

15 Students Only Batch Size

Eligibility

Anyone

Beginner Friendly

Beginner to Advanced Training

Course Curriculum

Excel formulas:

Learn basic Excel formulas like SUM, AVERAGE, and COUNTIF. Advanced to VLOOKUP, INDEX, and MATCH for data manipulation. Gain skills in data management, including removing duplicates and conditional formatting. Master creating charts and understand data organization through sorting and filtering. Explore Advanced filtering, Excel tables, and enhance formula usage. Focus on Advanced lookup functions and text manipulation. Master Pivot Tables for dynamic data analysis and Pivot Charts for effective visualization.

Arithmetic: Addition and Subtraction :

Arithmetic: Addition, subtraction, multiplication, and division basics. Weighted Average: Understanding and calculating weighted averages. Real-world applications and examples. Cumulative Sum: Definition and practical applications. Analyzing trends through cumulative sum. Percentile: Understanding and calculating percentiles. Application in data analysis. Basic Statistics: Mean: Arithmetic Mean: Calculating and its role. Weighted Mean: Calculations and applications. Median: Identifying and calculating median. Use in skewed distributions. Mode: Defining mode and identifying modes. Handling multimodal distributions. Standard Deviation: Understanding and calculating. Interpretation and implications. Normal Distribution: Identifying characteristics. Introducing Z-score for normalization.

SQL :

SELECT Statement: Retrieving data: SELECT column1, column2 FROM table; WHERE Clause: Filtering data: SELECT column1 FROM table WHERE condition; DISTINCT Keyword: Returning unique values: SELECT DISTINCT column FROM table; LIKE Operator: Pattern matching: SELECT column FROM table WHERE column LIKE pattern; BETWEEN Operator: Selecting within a range: SELECT column FROM table WHERE column BETWEEN value1 AND value2; ORDER BY Clause: Sorting results: SELECT column FROM table ORDER BY column ASC/DESC; LIMIT Keyword: Restricting rows: SELECT column FROM table LIMIT number; GROUP BY Clause: Grouping rows: SELECT column1, COUNT(column2) FROM table GROUP BY column1; HAVING Clause: Filtering grouped results: SELECT column1, COUNT(column2) FROM table GROUP BY column1 HAVING condition; INSERT Statement: Adding records: INSERT INTO table (column1, column2) VALUES (value1, value2); UPDATE Statement: Modifying records: UPDATE table SET column1 = value1 WHERE condition; ALTER Statement: Modifying table: ALTER TABLE table ADD COLUMN column_name datatype; IMPORT Statement: Loading data: IMPORT DATA INTO TABLE table FROM 'file_path'; Data Types: Defining column types: INTEGER, VARCHAR, DATE, BOOLEAN, etc.

Python :

Variables & Data Types: Storing and defining data. Collections: Lists, Tuples, Dictionaries, Sets. Conditional Statements & Loops: If Statements, For Loops, While Loops. Functions & Modules: Reusable code blocks, organizing code. Operators: Performing variable operations. OOP (Object-Oriented Programming): Classes and Objects for structured code. Python Libraries: Pandas: Read/Write Operations. DataFrames. Data Manipulation & Analysis. Matplotlib: Creating Visualizations (Static, Animated, Interactive).

Once you have completed the course you will be able to generate the certificate

  1. Attendance of at least 80% of the classes.

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