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Data Science
BeginnerFree LearningData Science

Data Science

by UptoSkills Team

Missions

13

Quests

82

Games

34

XP

700

Coins

35

Free Learning

Certificate optional — ₹499, or ₹199 on Pro

Self-paced
Certificate

Key Benefits

Earn XP & Level Up

Gain 700 XP to unlock badges and achievements

Collect Coins

Earn 35 coins to redeem rewards

Professional Certificate

QR-verified and recruiter-checkable — ₹499, or ₹199 on Pro

Unlock Achievements

Complete challenges and earn exclusive badges

About This League

Data Science involves extracting knowledge and insights from data. This beginner-level league provides a comprehensive foundation in the essential tools and techniques used in the field. It is designed for students aiming for placements and internships in data-related roles.

What you will learn

Upon completion, you will be able to:

  • Analyze and clean real-world datasets using Python and Pandas libraries.
  • Create informative data visualizations with Matplotlib to communicate insights.
  • Understand core statistical concepts and apply hypothesis testing.
  • Implement and evaluate fundamental machine learning algorithms like regression and classification.
  • Perform essential data preprocessing and model validation techniques for robust results.
  • Apply your skills to a comprehensive data analysis and prediction project.

What this league covers

ModuleWhat it covers
Introduction to Data ScienceUnderstand what data science is, its real-world applications, the data science process, essential skills, and setting up your development environment.
Python for Data Science: The BasicsLearn Python syntax, variables, data types, operators, control flow with if-else and loops, and defining functions.
Data Manipulation with PandasMaster Pandas DataFrames: creation, loading, selection, filtering, cleaning missing values, transformation, grouping, and merging.
Data Visualization with MatplotlibCreate and customize basic and advanced plots like line plots, scatter plots, histograms, bar charts, and subplots.
Statistical FoundationsGrasp descriptive statistics, measures of variability, probability basics, common distributions, hypothesis testing, correlation, and regression.
Introduction to Machine LearningExplore supervised and unsupervised learning, model evaluation metrics, the ML workflow, and the bias-variance tradeoff.
Linear RegressionUnderstand and implement simple and multiple linear regression, evaluate models, and learn about regularization techniques.
Logistic RegressionBuild and interpret logistic regression models for classification, understand regularization, and explore multiclass applications.
Decision TreesLearn to build, prune, and evaluate decision trees using splitting criteria like Gini and entropy, and understand their pros and cons.
Clustering with K-MeansUnderstand clustering, implement K-Means, choose the optimal number of clusters, and evaluate its assumptions and applications.
Data Preprocessing TechniquesMaster data cleaning, scaling, normalization, encoding categorical variables, feature selection, PCA, and data splitting.
Model Evaluation and ValidationDeep dive into cross-validation, regression and classification metrics, confusion matrices, ROC curves, AUC, and hyperparameter tuning.
Project: Data Analysis and PredictionExecute a full project from dataset overview and EDA to model building, evaluation, and reporting.

You will be equipped to perform data analysis and build predictive models.

A look inside

Data Science screen 1Data Science screen 2Data Science screen 3

Prerequisites

  • Comfortable writing Python, including lists, dictionaries and functions.
  • Comfortable with mean, median, distributions and basic probability.
  • Comfortable with algebra, percentages and reading a graph.

Learning Objectives

  • Understand the fundamental concepts of data science, including data types, data sources, and the data science pipeline.
  • Create basic data visualizations using Python libraries like Matplotlib and Seaborn to explore and communicate data insights.
  • Implement simple data cleaning techniques using Python and Pandas to handle missing values and inconsistencies in datasets.
  • Analyze datasets using descriptive statistics to identify patterns, trends, and outliers.
  • Apply introductory machine learning algorithms, such as linear regression, to predict outcomes based on input data.
  • Understand the ethical considerations involved in data collection, analysis, and application.