What will you learn in Learn Data Science Course
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Master the data analysis workflow from raw data to actionable insights.
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Use Python’s Pandas library for efficient data manipulation and cleaning.
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Handle missing values, detect outliers, and perform feature engineering.
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Create publication-quality visualizations with Matplotlib and Seaborn.
Program Overview
Module 1: Introduction to Data Analysis
⏳ 1.5 hours
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Topics: Overview of analysis lifecycle, data formats, project planning.
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Hands-on: Outline a data analysis project and explore sample datasets.
Module 2: Python & Pandas Essentials
⏳ 2 hours
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Topics: Series and DataFrame objects, indexing, filtering, merging.
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Hands-on: Load CSV/Excel data into Pandas and perform basic manipulations.
Module 3: Data Cleaning & Wrangling
⏳ 3 hours
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Topics: Handling missing data, outlier detection, type conversion, feature creation.
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Hands-on: Clean a messy dataset and engineer new variables for analysis.
Module 4: Exploratory Data Visualization
⏳ 2.5 hours
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Topics: Histograms, box plots, scatter plots, pair plots, and heatmaps.
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Hands-on: Visualize distributions and relationships to uncover insights.
Module 5: Statistical Analysis
⏳ 2.5 hours
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Topics: Descriptive statistics, correlation, hypothesis testing, confidence intervals.
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Hands-on: Compute summary metrics and perform t-tests and chi-square tests.
Module 6: SQL for Data Analysis
⏳ 2 hours
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Topics: SELECT statements, joins, aggregations, subqueries, window functions.
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Hands-on: Query a sample relational database to extract and summarize data.
Module 7: Time Series Analysis
⏳ 2 hours
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Topics: Date/time handling, rolling statistics, seasonal decomposition, simple forecasting.
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Hands-on: Analyze sales data over time and generate trend charts.
Module 8: Dashboarding & Reporting
⏳ 2 hours
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Topics: Designing dashboards, interactive widgets with Plotly or Streamlit basics.
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Hands-on: Build a simple dashboard to present key metrics.
Module 9: Capstone Project
⏳ 2.5 hours
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Topics: End-to-end project planning, execution, and presentation.
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Hands-on: Complete a full analysis—from data ingestion to a polished report—and share results.
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Job Outlook
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Data analysts are in strong demand across tech, finance, healthcare, and e-commerce.
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Roles such as Data Analyst, Business Intelligence Analyst, and Reporting Specialist typically command $70K–$100K USD.
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Expertise in Python, Pandas, SQL, and visualization tools accelerates career growth and unlocks remote and freelance opportunities.
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Strong analysis skills lead to paths in analytics engineering, data science, and digital reporting.
Explore More Learning Paths
Enhance your data science expertise with these carefully selected courses designed to provide practical skills, tools, and methodologies for analyzing data and making informed decisions.
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Tools for Data Science Course – Gain hands-on experience with essential data science tools and software for analysis, visualization, and modeling.
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Executive Data Science Specialization Course – Develop advanced data science skills for leadership roles and strategic decision-making.
Related Reading
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What Is Data Management – Understand how effective data management supports analysis, visualization, and informed business decisions.
