Course Material: IBM Applied Data Science
Course Material: IBM Applied Data Science
Course Outline
| Module | Topic | Subtopics |
|---|---|---|
| Module 1 | Introduction to Applied Data Science | – What is Applied Data Science? – The Role of a Data Scientist – Tools for Data Science – Understanding Data Science in the Real World |
| Module 2 | Data Collection and Cleaning | – Data Sources – Importing and Loading Data – Data Cleaning Techniques – Handling Missing Data and Outliers |
| Module 3 | Exploratory Data Analysis (EDA) | – Understanding Data Structure – Descriptive Statistics – Data Visualization with Python (Matplotlib, Seaborn) |
| Module 4 | Data Preparation and Feature Engineering | – Feature Scaling and Normalization – Feature Selection – Encoding Categorical Data – Handling Imbalanced Data |
| Module 5 | Introduction to Machine Learning | – Overview of Machine Learning – Types of Machine Learning Algorithms – Supervised and Unsupervised Learning – Preparing Data for ML |
| Module 6 | Supervised Learning Algorithms | – Regression Models (Linear, Logistic) – Decision Trees – Random Forest – Support Vector Machines (SVM) |
| Module 7 | Unsupervised Learning Algorithms | – Clustering Algorithms (K-Means, Hierarchical) – Principal Component Analysis (PCA) – Anomaly Detection |
| Module 8 | Model Evaluation and Validation | – Cross-Validation – Performance Metrics (Accuracy, Precision, Recall, F1-Score) – Overfitting and Underfitting |
| Module 9 | Deep Learning and Neural Networks | – Neural Network Basics – Convolutional Neural Networks (CNNs) – Recurrent Neural Networks (RNNs) – TensorFlow and Keras |
| Module 10 | Model Deployment and Operationalization | – Deploying Models into Production – Model Monitoring – Introduction to Cloud-based Model Deployment – Version Control for Models |
| Module 11 | Natural Language Processing (NLP) for Applied Data Science | – Text Preprocessing (Tokenization, Lemmatization) – Text Classification – Sentiment Analysis – Named Entity Recognition (NER) |
| Module 12 | Time Series Analysis and Forecasting | – Time Series Decomposition – ARIMA Models – Seasonal Trends and Forecasting – Evaluating Time Series Models |
| Module 13 | Big Data and Advanced Data Science Tools | – Introduction to Big Data Tools (PySpark) – Using Hadoop and Hive for Data Processing – Working with NoSQL Databases |
| Module 14 | Ethics in Applied Data Science | – Ethical Considerations in Data Science – Data Privacy and Security Concerns – Bias in Data Science Models |
| Module 15 | Capstone Project: Solving Real-World Problems | – Developing and Deploying End-to-End Data Science Projects – Project Review – Presentation of Results to Stakeholders |
Module 1: Introduction to Applied Data Science
Objective: This module introduces the concept of applied data science, focusing on practical approaches to solving real-world problems using data science techniques.
Key Topics:
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What is Applied Data Science?: Applied data science involves using data analysis, machine learning, and statistical methods to solve practical business and real-world problems. It goes beyond theoretical concepts and emphasizes results-driven techniques.
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The Role of a Data Scientist: Understand the responsibilities of a data scientist, which include data collection, cleaning, analysis, and building machine learning models.
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Tools for Data Science: Introduction to Python, Jupyter Notebooks, IBM Watson Studio, and other tools commonly used in applied data science.
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Data Science in the Real World: Explore how data science is applied in various industries, such as healthcare, finance, marketing, and retail.
Module 2: Data Collection and Cleaning
Objective: Data preparation is essential to successful data science projects. This module focuses on how to collect and clean data to make it ready for analysis.
Key Topics:
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Data Sources: Learn about various data sources, including structured data (databases, spreadsheets) and unstructured data (text, images, videos).
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Importing and Loading Data: How to load data into a Python environment using libraries like Pandas, NumPy, and SQL queries.
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Data Cleaning Techniques: Handling missing values, duplicates, and inconsistent data entries.
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Handling Missing Data and Outliers: Learn different strategies for dealing with missing data, including imputation and removal, as well as handling outliers that may skew the results.
Module 3: Exploratory Data Analysis (EDA)
Objective: This module focuses on understanding the structure of data and identifying patterns using statistical and visualization techniques.
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