What will you learn in Learn Data Analysis Course
Master the data analysis workflow from raw data to actionable insights.
Use Python’s Pandas library for efficient data manipulation and cleaning.
Handle missing values, detect outliers, and perform feature engineering.
Create publication-quality visualizations with Matplotlib and Seaborn.
Apply descriptive and inferential statistics to summarize and interpret data.
Program Overview
Module 1: Introduction to Data Analysis
⏳ 1.5 hours
Topics: Overview of analysis lifecycle, data formats, project planning.
Hands-on: Outline a data analysis project and explore sample datasets.
Module 2: Python & Pandas Essentials
⏳ 2 hours
Topics: Series and DataFrame objects, indexing, filtering, merging.
Hands-on: Load CSV/Excel data into Pandas and perform basic manipulations.
Module 3: Data Cleaning & Wrangling
⏳ 3 hours
Topics: Handling missing data, outlier detection, type conversion, feature creation.
Hands-on: Clean a messy dataset and engineer new variables for analysis.
Module 4: Exploratory Data Visualization
⏳ 2.5 hours
Topics: Histograms, box plots, scatter plots, pair plots, and heatmaps.
Hands-on: Visualize distributions and relationships to uncover insights.
Module 5: Statistical Analysis
⏳ 2.5 hours
Topics: Descriptive statistics, correlation, hypothesis testing, confidence intervals.
Hands-on: Compute summary metrics and perform t-tests and chi-square tests.
Module 6: SQL for Data Analysis
⏳ 2 hours
Topics: SELECT statements, joins, aggregations, subqueries, window functions.
Hands-on: Query a sample relational database to extract and summarize data.
Module 7: Time Series Analysis
⏳ 2 hours
Topics: Date/time handling, rolling statistics, seasonal decomposition, simple forecasting.
Hands-on: Analyze sales data over time and generate trend charts.
Module 8: Dashboarding & Reporting
⏳ 2 hours
Topics: Designing dashboards, interactive widgets with Plotly or Streamlit basics.
Hands-on: Build a simple dashboard to present key metrics.
Module 9: Capstone Project
⏳ 2.5 hours
Topics: End-to-end project planning, execution, and presentation.
Hands-on: Complete a full analysis—from data ingestion to a polished report—and share results.
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Job Outlook
Data analysts are in strong demand across tech, finance, healthcare, and e-commerce.
Roles such as Data Analyst, Business Intelligence Analyst, and Reporting Specialist typically command $70K–$100K USD.
Expertise in Python, Pandas, SQL, and visualization tools accelerates career growth and unlocks remote and freelance opportunities.
Strong analysis skills lead to paths in analytics engineering, data science, and digital reporting.
Specification: Learn Data Analysis Course
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FAQs
- Basic Python familiarity is recommended, but SQL and advanced analytics experience are not required.
- Designed for beginners in data analysis, business analytics, or related fields.
- Hands-on exercises guide learners from raw data cleaning to visualization and reporting.
- Covers Python, Pandas, SQL, Matplotlib, and Seaborn in practical workflows.
- Ideal for professionals transitioning to data-focused roles.
- Yes, the course teaches end-to-end data analysis workflows.
- Includes data cleaning, feature engineering, exploratory analysis, and dashboarding.
- Hands-on labs simulate real datasets from finance, healthcare, and e-commerce.
- Enables learners to produce actionable insights and business reports.
- Prepares learners for decision-making, reporting, and presentation tasks.
- Data Analyst, Business Intelligence Analyst, Reporting Specialist.
- Analytics Engineer or Junior Data Scientist with additional training.
- Freelance or remote analytics roles.
- Typical salaries range $70K–$100K USD depending on experience.
- Provides foundational skills to progress into advanced analytics or dashboarding roles.
- Focused on integrating Python, SQL, and visualization for end-to-end data workflows.
- Covers statistical analysis, time series, and interactive dashboards, not just coding syntax.
- Hands-on projects simulate real-world business problems.
- Unlike generic tutorials, it teaches complete data analysis lifecycle.
- Emphasizes actionable insights and professional reporting.
- Yes, covers Matplotlib, Seaborn, and introductory Plotly or Streamlit dashboards.
- Learners build interactive, publication-ready charts.
- Includes dashboards to summarize trends and key metrics.
- Prepares learners to communicate results to stakeholders effectively.
- Skills are applicable to business presentations and professional reports.
