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Data Science: Beginner

Data Science Track

Data Science:
Beginner

4.8 (1,230 reviews)

Introduction to Data Science Beginner. Learn data analysis, visualization, and basic Python for data. Get started with real-world datasets and tools like Excel and Pandas.

Created by Avanteia
12,580 Total Enrolled
15 September 2024 Last Updated
Enroll Now
Data Science Beginner Course
1 Month Duration
Certificate On Completion
Beginner Level
4 Modules Syllabus
1 Month Duration
English Language
Certificate Included
Introduction

Overview

Learn data analysis, visualization, and basic Python for data. Get started with real-world datasets and tools like Excel and Pandas.

Python Excel Visualization Pandas Statistics EDA
Goal

Learning Outcome

Learn data basics, Python, and visualization. Build simple models and explore datasets with hands-on tools.

Curriculum

Syllabus

Click any module to expand and view topics and hands-on labs included.

  • What is Data Science?
  • Real-world applications & career paths
  • Python fundamentals (variables, data types, loops, functions)
  • Libraries overview: NumPy, Pandas, Matplotlib
Hands-on Lab
Setup Google Colab/Jupyter Notebook Write Python programs (calculator, loops, list handling) Use Pandas to load & explore CSV data
  • Data collection (CSV, Excel, APIs, web scraping basics)
  • Cleaning missing values, duplicates, outliers
  • Encoding categorical data (One-hot, Label encoding)
  • Normalization & Standardization
Hands-on Lab
Load a dataset from Kaggle Perform data cleaning using Pandas Handle missing values & outliers
  • Graphs: bar, line, scatter, histogram, boxplot, heatmaps
  • Correlation analysis
  • Feature importance overview
Hands-on Lab
Use Matplotlib & Seaborn for visualization Create a heatmap of correlations Visualize trends in real-world dataset (COVID, Sales, etc.)
  • Descriptive statistics (mean, median, variance, std dev)
  • Probability distributions (Normal, Binomial, Poisson)
  • Hypothesis testing (t-test, chi-square test, ANOVA)
Hands-on Lab
Simulate coin toss & dice using Python Perform hypothesis testing on dataset in Colab
Results

What You Will Learn

Python for Data Science

Master Python fundamentals, NumPy, Pandas, and Matplotlib for data manipulation and analysis.

Data Handling & Preprocessing

Collect, clean, encode, and normalize data from various sources using industry-standard techniques.

Data Visualization & EDA

Create insightful visualizations and perform exploratory data analysis to uncover patterns and trends.

Probability & Statistics

Apply descriptive statistics, probability distributions, and hypothesis testing to real-world datasets.

Testimonials

What Our Students Say

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