Data Analyst
This comprehensive course covers essential tools and techniques for data analysis, equipping learners with hands-on experience in Excel, SQL, Python, Power BI, Tableau, and Machine Learning.
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Why Choose Codeintips?
Explore our top certification courses, trusted by thousands of student to boost your skill
Interactive Courses designed by experts
100% Placement Assistance
Limited Batch Strength
Interviews Preparation Sessions
One Year Live Session Access
Capstone Project
Work on Live Projects
24/7 Support For Seamless Learning
Grooming Session
Communication Session
CV Building
Mock Test
HR Interview Rounds
100% Practical Training
Comprehensive Curriculum
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Course Overview
Our comprehensive syllabus covers everything from the fundamentals to advanced techniques, ensuring you gain hands-on experience with real-world applications. Here’s what you’ll learn:
- What is Data Analytics?
- Role of a Data Analyst
- Types of Data Analytics (Descriptive, Diagnostic, Predictive, Prescriptive)
- Data Analytics Lifecycle
- Industry Use Cases (Finance, Healthcare, Marketing, E-commerce, etc.)
- Excel Basics: Functions & Formulas
- Data Cleaning & Formatting
- Pivot Tables & Pivot Charts
- VLOOKUP, HLOOKUP, INDEX-MATCH
- Conditional Formatting & Data Validation
- Power Query for Data Transformation
- Data Analysis ToolPak (Regression, Correlation)
- Dashboard Creation
- Introduction to Databases & SQL
- Writing Queries (SELECT, WHERE, ORDER BY, GROUP BY, HAVING)
- Joins (INNER, LEFT, RIGHT, FULL OUTER)
- Subqueries & Common Table Expressions (CTEs)
- Window Functions (RANK, DENSE_RANK, PARTITION BY)
- Data Cleaning in SQL
- Views & Stored Procedures
- Connecting SQL to Power BI/Tableau
- Introduction to Python & Jupyter Notebook
- Data Manipulation with Pandas
- Data Cleaning Techniques (Handling Missing Data, Outliers)
- Data Visualization using Matplotlib & Seaborn
- Working with APIs & JSON Data
- Web Scraping with BeautifulSoup
- Automating Data Tasks with Python
- Descriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation)
- Probability Distributions (Normal, Binomial, Poisson)
- Hypothesis Testing (t-tests, ANOVA, Chi-Square)
- Correlation vs. Causation
- Confidence Intervals & p-values
- A/B Testing in Data Analytics
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