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Data Analyst Course

This comprehensive Data Analyst course is designed to equip learners with the essential skills and tools needed to collect, analyze, and interpret data to drive informed business decisions. Through a combination of theoretical instruction, practical exercises, and real-world projects, students will gain proficiency in data wrangling, statistical analysis, data visualization, and the use of industry-standard tools such as Excel, SQL, Python, and Tableau.

The course begins with foundational concepts in data analysis and progresses to more advanced topics, including predictive analytics and data storytelling. Learners will work with diverse datasets and build a portfolio of projects that demonstrate their ability to translate data into actionable insights.

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💡 Course Objectives

By the end of this course, participants will be able to

Understand the data analysis process and lifecycle.

Clean, organize, and manipulate datasets using tools like Excel, SQL, and Python.

Perform exploratory data analysis and apply statistical techniques.

Create impactful data visualizations using tools such as Tableau or Power BI.

Communicate insights effectively to both technical and non-technical stakeholders.

Build dashboards and reports for ongoing data monitoring.

🛠️ Course Features

100% practical and
project-based learning

Real-world
capstone projects

Code reviews and
mentor support

Resume and GitHub
portfolio development

Lifetime access to
course materials (if online)

Certification upon
successful completion

👨 Who Should Take This Course

This course is ideal for

  • Aspiring data analysts
  • Business professionals seeking data-driven decision-making skills
  • Students and recent graduates exploring a career in data analytics
  • Professionals transitioning into data-focused roles

👨 Prerequisites

  • Basic computer literacy
  • Familiarity with spreadsheets (Excel or Google Sheets)
  • No prior coding experience required (introductory programming included)

📦 Tools & Technologies Covered

Microsoft Excel / Google Sheets

SQL

Python (Pandas, NumPy, Matplotlib, Seaborn)

Tableau or Power BI

Jupyter Notebooks

GitHub (for project versioning and portfolio)

🔧 Data Analyst Course Tools

🗂️ Data Collection & Storage
  • SQL (Structured Query Language) – For querying and managing data in relational databases (e.g., MySQL, PostgreSQL, SQLite).
  • Google Sheets / Microsoft Excel – For initial data manipulation, cleaning, and analysis.
  • Python – A versatile programming language for data analysis using libraries like:
  • Pandas – For data manipulation and cleaning.
  • NumPy – For numerical operations.
  • Matplotlib & Seaborn – For data visualization.
  • Scikit-learn – For basic machine learning and predictive analytics.
  • R (optional/alternative to Python) – Another language used for statistical analysis and visualization.
  • Tableau – A leading data visualization tool for creating interactive dashboards.
  • Power BI – A Microsoft tool for business analytics and reporting.
  • Excel (Charts & Pivot Tables) – Still widely used for basic visualization and quick reporting.
  • Jupyter Notebook – An interactive coding environment for Python.
  • Google Colab – A cloud-based alternative to Jupyter, ideal for beginners and collaboration.
  • VS Code (optional) – A code editor for Python and SQL.
  • Git – Version control system for tracking changes.
  • GitHub – For storing code and collaborating on projects.
  • APIs – For accessing data from web services.
  • Google Analytics / GA4 – For web traffic and user behavior analysis.
  • Looker Studio (formerly Google Data Studio) – For creating custom dashboards and reports.

🧾 Data Analyst Course Content

1. Introduction to Data Analytics
  • What is data analysis?
  • Role of a data analyst
  • Types of data (structured vs. unstructured)
  • The data analysis process (collect, clean, analyze, visualize, interpret)
  • Formulas and functions
  • Data cleaning techniques
  • Pivot tables and charts
  • Conditional formatting and lookups (VLOOKUP, HLOOKUP, XLOOKUP)
  • Relational databases and tables
  • Basic queries (SELECT, WHERE, ORDER BY)
  • Joins (INNER, LEFT, RIGHT)
  • Aggregations (COUNT, SUM, GROUP BY)
  • Subqueries and nested queries
  • Python basics (variables, data types, loops, functions)
  • Pandas for data frames
  • NumPy for numerical operations
  • Data wrangling and transformation
  • Handling missing or incorrect data
  • Descriptive statistics (mean, median, mode, std dev)
  • Outlier detection
  • Data distributions
  • Correlations and relationships
  • Principles of effective visualization
  • Creating charts in: Excel Python (Matplotlib, Seaborn) Tableau or Power BI
  • Dashboards and interactive reports
  • Probability basics
  • Distributions (normal, binomial)
  • Hypothesis testing
  • Confidence intervals
  • Regression analysis (linear regression)
  • Data cleaning and analysis on real datasets
  • Building dashboards and presenting insights
  • Case studies (e.g., sales analysis, customer behavior, marketing ROI)
  • Portfolio development
  • Basics of machine learning (classification, clustering)
  • Working with APIs and web scraping
  • Introduction to cloud tools (BigQuery, Snowflake)
  • Version control with Git and GitHub
  • By the end of the course, learners should be able to
  • Collect and clean data
  • Analyze and visualize insights
  • Use tools like SQL, Excel, Python, and Tableau/Power BI
  • Build and present analytical projects confidently

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Ideal for fast-paced learning with daily live sessions and hands-on projects

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