Best Data Science Course Online with Certification
Course Duration: 12 Weeks (60 Hours Total)
Data Science Course Modules
This course provides a solid foundation in data science, covering essential mathematical concepts, core techniques, tools, and real-world applications. It is designed for aspiring data scientists, analysts, and anyone interested in gaining a deep understanding of data science.
1) Introduction to Data Science:
– What is Data Science? Overview and Applications
– The Data Science Lifecycle: Data Collection, Preparation, Modeling, Evaluation, and Deployment
– Tools and Technologies in Data Science: Python, R, Jupyter Notebooks, etc.
– Roles in Data Science: Data Scientist, Data Analyst, Data Engineer, Machine Learning Engineer
– Overview of Key Domains: Finance, Healthcare, Marketing, and Social Sciences
2) Mathematical Foundations for Data Science:
– Statistics:
– Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
– Probability Theory: Probability Distributions, Bayes’ Theorem
– Inferential Statistics: Hypothesis Testing, Confidence Intervals
– Correlation and Regression Analysis
– Linear Algebra:
– Vectors and Matrices: Operations, Transpose, Inverse
– Eigenvalues and Eigenvectors
– Linear Transformations and Projections
– Applications of Linear Algebra in Data Science
– Calculus:
– Basics of Differentiation and Integration
– Partial Derivatives and Gradient Descent
– Optimization Techniques: Gradient Descent and Stochastic Gradient Descent (SGD)
– Applications of Calculus in Machine Learning
3) Data Manipulation and Analysis:
– Data Cleaning: Handling Missing Data, Outliers, and Inconsistencies
– Data Wrangling with Pandas: DataFrames, Series, Indexing, Merging, and Grouping
– Exploratory Data Analysis (EDA): Data Visualization with Matplotlib and Seaborn
– Feature Engineering: Feature Extraction, Selection, and Dimensionality Reduction
– Dealing with Big Data: Apache Spark and Dask
4) Data Visualization:
– Principles of Data Visualization: Best Practices and Common Pitfalls
– Data Visualization Tools: Matplotlib, Seaborn, Plotly, and Bokeh
– Creating Interactive Dashboards with Power BI and Tableau
– Advanced Visualization Techniques: Geospatial Data, Network Graphs, and Time-Series Analysis
– Storytelling with Data: Crafting Effective Visual Narratives
5) Introduction to Machine Learning :
– What is Machine Learning? Types: Supervised, Unsupervised, Reinforcement Learning
– Key Algorithms: Linear Regression, Logistic Regression, Decision Trees, k-Nearest Neighbors (k-NN)
– Model Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, AUC-ROC
– Cross-Validation and Hyperparameter Tuning
– Overfitting, Underfitting, and Bias-Variance Tradeoff
6) Advanced Machine Learning Techniques:
– Ensemble Methods: Bagging, Boosting, Random Forests, and Gradient Boosting Machines (GBMs)
– Support Vector Machines (SVMs) and Kernel Methods
– Clustering Techniques: k-Means, Hierarchical Clustering, DBSCAN
– Dimensionality Reduction: Principal Component Analysis (PCA), t-SNE
– Introduction to Deep Learning: Neural Networks, Backpropagation, and Optimization
7) Natural Language Processing (NLP):
– Introduction to NLP: Text Preprocessing and Tokenization
– Feature Extraction from Text: Bag of Words, TF-IDF, Word Embeddings
– NLP Algorithms: Sentiment Analysis, Named Entity Recognition, Text Classification
– Topic Modeling: Latent Dirichlet Allocation (LDA)
– Advanced NLP with Deep Learning: Transformers, BERT, GPT
8) Time Series Analysis and Forecasting:
– Introduction to Time Series Data: Components and Patterns
– Time Series Decomposition and Smoothing Techniques
– Forecasting Methods: ARIMA, SARIMA, Exponential Smoothing
– Evaluation Metrics for Time Series Forecasting
– Advanced Methods: LSTM (Long Short-Term Memory) Networks and Prophet
9) Deep Learning and Neural Networks:
– Understanding Neural Networks: Architecture, Activation Functions, Loss Functions
– Building and Training Deep Learning Models with TensorFlow and Keras
– Convolutional Neural Networks (CNNs) for Image Classification
– Recurrent Neural Networks (RNNs) for Sequence Modeling
– Transfer Learning and Fine-Tuning Pre-trained Models
10) Big Data and Scalable Data Science:
– Introduction to Big Data Technologies: Hadoop, Spark, and NoSQL Databases
– Distributed Computing and Parallel Processing
– Working with Big Data Tools: Hive, Pig, HDFS, and Apache Kafka
– Scalable Machine Learning with Apache Spark MLlib
– Managing Data Pipelines and Workflow Orchestration: Apache Airflow
11) Real-World Data Science Applications:
– Case Studies in Different Industries: Healthcare, Finance, Marketing, etc.
– Building Recommendation Systems
– Fraud Detection with Machine Learning
– Customer Segmentation and Market Basket Analysis
– Image Recognition and Object Detection
12) Hands-On Labs and Projects:
– Implementing a Machine Learning Model from Scratch
– Building a Real-Time Data Pipeline with Apache Kafka and Spark
– Developing an Interactive Dashboard for Data Visualization
– Creating a Predictive Model for a Business Problem
– Applying NLP to Sentiment Analysis and Text Classification
13) Capstone Project:
– Comprehensive Data Science Project: End-to-End Data Pipeline Development
– Presentation of Findings, Methods, and Insights
– Peer Review and Feedback Session
14) Assessment and Certification
– Final Exam: Multiple Choice and Scenario-Based Questions
– Practical Assessment: Building and Deploying a Machine Learning Model
– Course Completion Certificate
Learning Outcomes
By the end of this course, participants will:
– Master the foundational mathematics and statistics needed for data science
– Gain proficiency in data manipulation, visualization, and analysis techniques
– Learn to build, evaluate, and deploy machine learning models
– Understand deep learning concepts and their applications
– Be capable of applying data science to real-world problems