DATA-SCIENCE.AW1

Learn Data Science from Scratch

Data science made simple. Simple to grasp. And grasp the future with both hands.

19 Interactive Lessons and 172 topics mapped to the official exam objectives

Beginner Self-paced · 1 year access

19Interactive Lessons

172Topics

01 / Skills you'll get

What you will be able to do

Our Data Science for Beginners course guides you through a hands-on journey from Python basics to machine learning.

Through interactive lessons, you’ll tackle data scraping, visualization, and cleaning. As you progress, you can test your knowledge of ML algorithms, NLP, and real-world project deployment in virtual labs.

So, gear up to become a certified data scientist.

Python Programming for data science, including NumPy, Pandas, and Matplotlib/Seaborn.
Data collection techniques like web scraping and API integration.
Exploratory Data Analysis (EDA) and data visualization to uncover patterns.
Data cleaning and preprocessing to handle missing values, normalization, and feature engineering.
Statistics and probability fundamentals, including distributions, hypothesis testing, and Bayes’ theorem.
ML algorithms, including regression (linear, logistic), k-NN, Naive Bayes, decision trees, and SVMs.
Dimensionality reduction with PCA, t-SNE, and UMAP
Unsupervised learning via clustering (k-means, DBSCAN)
Deep learning basics such as neural networks, CNNs, RNNs, LSTMs
Natural Language Processing (NLP): text processing, sentiment analysis, topic modeling
Building recommender systems (collaborative/content-based filtering, hybrid models)
Database management (SQL, NoSQL) and data storage (warehouses, lakes)
End-to-end project deployment, from data pipelines to model monitoring

Course Highlights

19 Structured Lessons Comprehensive coverage of core course objectives
1 Year Full Access Self-paced learning accessible anytime on all devices

02 / Lessons & labs

See exactly what you will learn and practice

Download outline (PDF)

Lessons

19 Interactive Lessons · 172 topics

01 Introduction

02 Unraveling the Data Science Universe: An Introduction 11 topics

Introduction
What is data science
Data science: A fusion of fields
History and evolution of data science as a field
The data science process
A day in the life of a data scientist
How data science is shaping our world
Differences between Artificial Intelligence, big data, and data science
Conclusion
Points to remember
Questions

03 Essential Python Libraries and Tools for Data Science 10 topics

Introduction
Setting up your developer environment
Basics of NumPy
Pandas for data manipulation
Matplotlib, seaborn, and Plotly for data visualization
Jupyter Notebook essentials
Scikit-learn: Key to streamlined Machine Learning
Conclusion
Points to remember
Questions

04 Statistics and Probability Essentials for Data Science 13 topics

Introduction
Probability theory
Basic probability concepts
Conditional probability and Bayes’ theorem
Discrete and continuous random variables
Expectation, variance, and covariance of random variables
Distributions and sampling
Central limit theorem
Sampling techniques
Hypothesis testing
Conclusion
Points to remember
Questions

05 Data Mining Expedition: Web Scraping and Data Collection Techniques 8 topics

Introduction
Sources of data
Web scraping with Beautiful Soup and Requests
APIs and Python libraries for data collection
Ethical considerations during data collection
Conclusion
Points to remember
Questions

06 Painting with Data: Exploration and Visualization 8 topics

Introduction
Exploratory data analysis
Descriptive statistics
Data visualization with Matplotlib, seaborn, and Plotly
Discovering trends and relationships
Conclusion
Points to remember
Questions

07 Data Alchemy: Cleaning and Preprocessing Raw Data 9 topics

Introduction
Handling missing data
Data transformation and normalization
Addressing duplication and data inconsistencies
Feature engineering and selection
Encoding categorical features
Conclusion
Points to remember
Questions

08 Machine Learning Magic: An Introduction to Predictive Modeling 8 topics

Introduction
Supervised and unsupervised learning
Essential algorithms and model selection
Training, testing, and evaluation'
Overfitting and underfitting
Conclusion
Points to remember
Questions

09 Exploring Regression: Linear, Logistic, and Advanced Methods 7 topics

Introduction
Linear regression
Logistic regression
Harnessing regularization: Techniques to rein in your model
Conclusion
Points to remember
Questions

10 Unveiling Patterns with k-Nearest Neighbors and Naïve Bayes 7 topics

Introduction
Understanding the k-Nearest Neighbors algorithm
Naïve Bayes classifier
Hyperparameter tuning
Conclusion
Points to remember
Questions

11 Exploring Tree-Based Models: Decision Trees to Gradient Boosting 8 topics

Introduction
Decision trees
Entropy and information gain
Tree pruning and optimization
The power of ensemble methods in machine learning
Conclusion
Points to remember
Questions

12 Support Vector Machines: Simplifying Complexity 9 topics

Introduction
Introduction to support vector machines
Understanding kernel methods
SVM for classification and regression roles
Real-world SVM: From preprocessing to evaluation
Balancing the bias-variance trade-off in SVM
Conclusion
Points to remember
Questions

13 Dimensionality Reduction: From PCA to Advanced Methods 8 topics

Introduction
Understanding the problem of high dimensionality
Principal component analysis
Visualizing high-dimensional data
Exploring beyond PCA: t-SNE and UMAP
Conclusion
Points to remember
Questions

14 Unlocking Unsupervised Learning 9 topics

Introduction
K-means clustering
Hierarchical clustering
Understanding DBSCAN: A comprehensive guide
DBSCAN and other density-based methods
Cluster evaluation and validation
Conclusion
Points to remember
Questions

15 The Essence of Neural Networks and Deep Learning 15 topics

Introduction
Deep learning: Beyond conventional machine learning
Deep learning as artificial intelligence’s game changer
Data and processing power
Introduction to deep learning libraries
The intricate web of artificial neural networks
Importance of data and feature engineering in deep learning
Feature crafting versus self-learning
Overfitting: A deep learning perspective
Convolutional neural networks
Recurrent neural networks
Long short-term memory networks
Conclusion
Points to remember
Questions

16 Word Play: Text Analytics and Natural Language Processing 8 topics

Introduction
Text processing and tokenization
The transformation journey: From text to features
Decoding emotions: Sentiment analysis and text classification
Topic modeling and entity recognition
Conclusion
Points to remember
Questions

17 Crafting Recommender Systems 15 topics

Introduction
Introduction to collaborative filtering
User-based collaborative filtering
Decoding item-based collaborative filtering
Measuring similarities in recommender systems
Sparsity and scalability in collaborative filtering
Building your first collaborative filtering systems in Python
Personalized proposals: Understanding content-based filtering
Building content based recommendations in Python
Matrix factorization and SVD in recommender system
Synergy in recommendation: Hybrid systems
Crafting a hybrid recommender with Python: Step-by-step guide
Conclusion
Points to remember
Questions

18 Data Storage Mastery: Databases and Efficient Data Management 11 topics

Introduction
Exploring database types: Relational and NoSQL databases
Diversifying your data storage: NoSQL databases
Python meets SQL: Mastering database interaction
Navigating databses in Python: SQLAlchemy, SQLite3, PyMango
Python data format handling: CSV, JSON, XML, Parquet, Excel
Unpacking serialization: Moving and storing data efficiently
Data warehouses and data lakes: A comprehensive guide
Conclusion
Points to remember
Questions

19 Data Science in Action: A Comprehensive End-to-end Project 8 topics

Introduction
Defining a data science problem
Data collection and preparation
From selection to evaluation: Charting the model’s journey
Communication of results
Deployment, monitoring and maintenance of a model
Conclusion
Points to remember

03 / FAQs

Questions before you start

Will I learn enough to get a job as a data scientist?

This Data Science Training for Beginners covers foundational skills (Python, ML, stats), but entry-level jobs may require portfolio projects and additional practice.

Can I take this course if I’m not a programmer?

Yes, but expect a steeper learning curve. Prioritize the Python basics modules first.

Is there a certificate? Can I add it to LinkedIn?

Yes! You will receive a shareable certificate of completion, which is great for resumes and LinkedIn.

Will I learn practical applications of data science?

Yes, The course includes hands-on labs and simulations to help you practice building projects like:

Predictive models
NLP applications
Recommender systems

Data Science Starts Here (So Should You)

Your Excel skills are cute. But it’s time to level up. Enroll in our beginner data science course today!

1 year of full access
Certificate of completion