Curriculum
15 Sections
59 Lessons
10 Weeks
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Module 1: Introduction to Data Science and Python
4
1.0
Overview of data science: Concepts and applications
1.1
Introduction to Python: Installation and setup
1.2
Python programming fundamentals: Data types, variables, loops, and functions
1.3
Understanding the data science workflow
Module 2: Python Libraries for Data Science
4
2.0
Introduction to NumPy: Working with arrays and numerical data
2.1
Introduction to Pandas: Data manipulation and dataframes
2.2
Data input and output: Reading and writing data in various formats
2.3
Data cleaning and preprocessing using Pandas
Module 3: Data Manipulation and Cleaning
4
3.0
Handling missing data and outliers
3.1
Data transformation: Aggregation, filtering, and sorting
3.2
Data merging and concatenation
3.3
Dealing with categorical and datetime data
Module 4: Exploratory Data Analysis (EDA)
4
4.0
Techniques for summarizing and visualizing data
4.1
Univariate and bivariate analysis
4.2
Detecting patterns, trends, and correlations
4.3
Visualizing distributions with histograms, box plots, and scatter plots
Module 5: Statistical Analysis with Python
4
5.0
Basics of probability and statistics for data science
5.1
Descriptive statistics: Mean, median, mode, variance, and standard deviation
5.2
Inferential statistics: Hypothesis testing, confidence intervals, and p-values
5.3
Correlation and regression analysis
Module 6: Introduction to Machine Learning
4
6.0
Overview of machine learning: Types of learning and algorithms
6.1
Supervised learning: Regression and classification techniques
6.2
Unsupervised learning: Clustering and dimensionality reduction
6.3
Introduction to Scikit-Learn: Building and evaluating machine learning models
Module 7: Supervised Learning Techniques
4
7.0
Linear regression: Concepts, implementation, and evaluation
7.1
Logistic regression: Binary classification and probability prediction
7.2
Decision trees and random forests: Intuitive, easy-to-understand models
7.3
Model evaluation techniques: Cross-validation, ROC curves, and confusion matrices
Module 8: Unsupervised Learning Techniques
3
8.0
K-means clustering: Grouping data points into clusters
8.1
Hierarchical clustering: Understanding nested clusters
8.2
Anomaly detection: Identifying outliers in datasets
Module 9: Deep Learning with Python
4
9.0
Introduction to neural networks and deep learning
9.1
Building deep learning models with TensorFlow and Keras
9.2
Convolutional Neural Networks (CNN) for image recognition
9.3
Recurrent Neural Networks (RNN) for time-series forecasting
Module 10: Data Visualization
4
10.0
Introduction to data visualization: Importance and best practices
10.1
Plotting with Matplotlib and Seaborn: Line plots, bar charts, heatmaps, and more
10.2
Interactive visualizations with Plotly and Dash
10.3
Creating dashboards and reports for data communication
Module 11: Time Series Analysis
4
11.0
Introduction to time series data: Characteristics and challenges
11.1
Time series decomposition and forecasting methods
11.2
Implementing ARIMA and SARIMA models
11.2
Applying machine learning to time series forecasting
Module 12: Real-World Data Science Projects
4
12.0
Hands-on project: End-to-end data science workflow
12.1
Case study 1: Predictive modeling for business decision-making
12.2
Case study 2: Customer segmentation using clustering techniques
12.3
Presentation and feedback on project work
Module 13: Advanced Topics in Data Science\\
4
13.0
Natural Language Processing (NLP): Text processing and sentiment analysis
13.1
Big Data and Cloud Computing: Handling large datasets with Hadoop and Spark
13.2
Introduction to reinforcement learning
13.3
Ethical considerations and best practices in data science
Module 14: Data Science in Business
4
14.0
Application of data science in various industries: Finance, healthcare, retail, etc.
14.1
Building data-driven business strategies
14.2
Communicating data science insights to non-technical stakeholders
14.3
Case studies on the impact of data science in business
Module 15: Career Development and Placement Support
4
15.0
Preparing for data science interviews: Common questions and best practices
15.1
Resume building and portfolio development
15.2
Networking and job search strategies in the data science field
15.3
100% placement assistance: Connecting with potential employers and recruiters
Data Science with Python
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