A Fast-Track Course in Data Science
and Machine Learning
This intensive program is designed for learners who want to gain a solid understanding of
data science and machine learning in a short period of time.
Course Description
This intensive program is designed for learners who want to gain a solid understanding of data science and machine learning in a short period of time. Students will learn how to collect and analyze data, develop predictive models, and make data-driven decisions. The program covers a range of topics including data analysis, programming languages, statistical inference, machine learning algorithms, and model evaluation. This course provides a quick and practical introduction to data science and machine learning, with a focus on real-world applications.
Course Objectives
Demonstrate an understanding of fundamental data science concepts and techniques.
- Develop programming skills in Python and provide hands-on experience with popular Python.
libraries for data science and machine learning. - Understand descriptive and inferential statistics and their applications in data science and
machine learning. - Implement various machine learning algorithms and techniques for model building and
evaluation. - Provide hands-on experience in building machine learning models.
- Provide an overview of advanced concepts in Data Science such as Neural networks, Text
Mining and Time Series Analysis.
Learning Outcomes
After successful completion of this program, a learner will be able to
- Apply fundamental concepts of Data Science and Machine Learning to solve business problems.
- Use popular Python libraries for data science and machine learning such as NumPy, Pandas,
Matplotlib, Seaborn, and Scikit-learn - Apply descriptive statistics and probability distributions to analyze data and create
visualizations to gain insights. - Apply machine learning algorithms using Python for supervised and unsupervised learning.
- Build and implement machine learning algorithms for predictive modeling and clustering.
- Write basic python codes to deal with text data and time series data.
Conclusion
This program is designed for professionals who want to upskill quickly in Data Science and Machine Learning. The program covers the essential concepts, techniques, and tools required to build and deploy ML models. It covers a comprehensive range of topics to help participants develop a strong foundation in these fields and enhance their career prospects. Upon completion of this course, students will have the knowledge and skills necessary to pursue a career in this exciting and rapidly growing field.
1) Operators and built-in Functions (1 Hours)
2) Control Structures (1 Hours)
3) Functions (1 Hours)
4) Strings (1 Hours)
5) Lists, Tuples, Sets and Dictionaries (4 Hours)
1) NumPy (2 Hours)
2) Pandas (6 Hours)
3) Matplotlib (3 Hours)
4) Seaborn (3 Hours)
1) Nature of Data (1 Hours)
2) Data Visualization (1 Hours)
3) Summary Statistics (2 Hours)
4) Bivariate Data Analysis and Correlation (2 Hours)
1) Classical Probability (1 Hours)
2) Conditional Probability (1 Hours)
3) Random Variables (1 Hours)
4) Discrete Distributions (2 Hours)
5) Continuous Distributions (2 Hours)
6) Applications of Normal Distribution (1 Hours)
1) Introduction to Statistical Inference (1 Hours)
2) Confidence Interval (1 Hours)
3) Hypothesis Testing (1 Hours)
4) Common Statistical Tests (4 Hours)
5) ANOVA (1 Hours)
1) Introduction to Machine Learning (2 Hours)
2) Exploratory Data Analysis (4 Hours)
3) Data Preprocessing (2 Hours)
4) Model Building and Evaluation Metrics (2 Hours)
5) K-Nearest Neighbourhood (2 Hours)
6) Linear Regression (3 Hours)
7) Multiple Regression (5 Hours)
8) Logistic Regression (3 Hours)
9) Naïve Bayes (2 Hours)
10) Decision Trees (3 Hours)
11) Ensemble Techniques (2 Hours)
12) Random Forests (1 Hours)
13) Support Vector Machine (2 Hours)
14) K-Means Clustering (2 Hours)
15) Hierarchical Clustering (2 Hours)
16) Principal Component Analysis (2 Hours)
17) Regularization Techniques (2 Hours)
18) Association Mining (2 Hours)
19) Recommendation Engines (2 Hours)
20) Miscellaneous Topics (3)
1) Gradient Descent Algorithm (2 Hours)
2) Artificial Neural Network (4 Hours)
1) Introduction to Text Mining (2 Hours)
2) Applications of Text Analytics (4 Hours)
1) Components of Time Series (1 Hours)
2) Visualizing Time Series (1 Hours)
3) Moving Average Method (1 Hours)
4) Introduction to Forecasting Methods (1 Hours)
5) Applications of Time Series Analysis (2 Hours)