Data Science Nanodegree

About this course

Throughout this course, you will explore the complete data science lifecycle, from data collection to the communication of insights. Key topics include:
Data Collection: Learn how to gather data from various sources, including APIs and web scraping techniques. Data
Cleaning and Preparation: Understand the importance of preprocessing data, handling missing values, and ensuring data quality.
Exploratory Data Analysis (EDA): Discover how to visualize data and identify patterns, trends, and anomalies that inform decision-making.
Statistical Analysis: Gain insights through statistical methods, including regression analysis and hypothesis testing.
Machine Learning Fundamentals: Introduce yourself to the world of machine learning, exploring supervised and unsupervised learning techniques and how to build predictive models.
Data Visualization: Master the art of storytelling with data through compelling visualizations using tools like Matplotlib and Tableau.
Model Deployment and Monitoring: Understand how to implement and maintain data models in a production environment, ensuring they deliver ongoing value.
Effective Communication: Learn how to present your findings to stakeholders clearly and persuasively, highlighting the implications of your analysis.

By the end of this course, you will have a solid understanding of data science principles and the practical skills necessary to analyze data effectively. Join us and begin your journey into the exciting world of data science, where your insights can drive impactful change across industries.

Requirements

No prior skill prerequisites — beginner to advanced
A personal computer — Mac, Windows, or Linux
A stable internet connection for live classes and downloads
Dedication and time

Course curriculum — 15 modules

Module 1

Introduction to Data Science

Overview of Data Science: Definition, importance, and real-world applications
Data Science workflow: Data collection, cleaning, analysis, visualization, and interpretation
Key tools in Data Science: Python, R, SQL, and more

Module 2

Statistics and Probability

Descriptive statistics: Mean, median, mode, variance, and standard deviation
Inferential statistics: Hypothesis testing, confidence intervals
Probability theory: Concepts, distributions, and theorems
Random variables: Discrete and continuous distributions
Bayes' Theorem

Module 3

Basics of Machine Learning

Introduction to Machine Learning: Supervised, unsupervised, and reinforcement learning
Overview of Machine Learning algorithms: Classification, regression, clustering, and more
Model training, validation, and testing
Bias-variance tradeoff
Overfitting and underfitting

Module 4

Linear Regression

Simple and multiple linear regression
Assumptions of linear regression
Evaluating regression models: R-squared, Mean Squared Error (MSE), etc.
Feature scaling and normalization

Module 5

Logistic Regression

Introduction to logistic regression for binary classification
Sigmoid function and probability interpretation
Maximum likelihood estimation
Evaluating model performance: Confusion matrix, precision, recall, F1-score, AUC-ROC curve

Module 6

Decision Trees

Understanding decision tree structure: Nodes, branches, leaves
Splitting criteria: Gini index, entropy, and information gain
Pruning techniques to prevent overfitting
Advantages and limitations of decision trees

Module 7

Random Forest

Introduction to ensemble methods
How Random Forest works: Bagging and boosting
Feature importance in Random Forest
Hyperparameter tuning for Random Forest

Module 8

K-Nearest Neighbors (KNN)

Distance metrics: Euclidean, Manhattan, and others
Choosing the right K value
Strengths and limitations of KNN
Applications of KNN in real-world scenarios

Module 9

Naive Bayes

Bayes' Theorem and its application in classification
Assumptions of Naive Bayes
Types of Naive Bayes classifiers: Gaussian, Multinomial, Bernoulli
Use cases of Naive Bayes in spam filtering, sentiment analysis, etc.

Module 10

Support Vector Machine (SVM)

Concept of hyperplanes and support vectors
Linear and non-linear SVM
Kernel functions: Polynomial, RBF, etc. Soft margin vs hard margin classification

Module 11

K-Means Clustering

Introduction to clustering techniques
K-Means algorithm: Centroid calculation and cluster assignment
Choosing the optimal number of clusters with the Elbow method
Limitations and applications of K-Means

Module 12

Association Rule Mining

Market basket analysis and association rules
Apriori algorithm for association rule learning
Support, confidence, and lift
Applications of association rules in retail and e-commerce

Module 13

Reinforcement Learning

Basic concepts of reinforcement learning: Agents, states, actions, rewards
Exploration vs exploitation dilemma
Markov Decision Processes (MDPs)
Q-learning and policy-based methods

Module 14

Deep Learning

Introduction to neural networks and deep learning
Types of neural networks: Feedforward, Convolutional (CNN), Recurrent (RNN)
Backpropagation and gradient descent
Activation functions: Sigmoid, ReLU, Softmax
Introduction to frameworks like TensorFlow and PyTorch

Module 15

Data Science Interview Questions

Common interview questions on data science concepts, algorithms, and techniques
Hands-on exercises to practice problem-solving and coding
Tips for technical interviews: How to approach machine learning problems, discuss models, and optimize solutions
Behavioral interview preparation: Communicating projects, teamwork, and challenges faced

Dr. Richards M.

Data Engineer · University of Derby, UK

5.0 · 1,500+ Students

Dr. Richards M. is a skilled Data Analytics professional with a Master's in Big Data Analytics from the University of Derby, UK. He has expertise across various Engineering Tech Stacks, contributing to process optimization and market enhancement for organizations, with a strategic approach focused on business growth and efficiency.

Student reviews

Adenike Idowu

4.9

Empowering and Educative!

Wivina Omolemen

4.9

Life-Changing Educational Journey!

As a recent graduate, Vephla University played an instrumental role in my academic success. The interactive learning modules made difficult concepts easier to grasp, and I loved how I could learn at my own pace.

Data Science Nano-Degree

About this course

Requirements

Course curriculum — 15 modules

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