Global Certificate in Intensive Data Science with Python

This course is tailored for professionals and students seeking to deepen their data science skills using Python. Ideal for those with some programming background, it equips participants with advanced Python programming skills, statistical analysis, machine learning, and data visualization techniques.

By the end of this course, learners will gain proficiency in handling large datasets, applying machine learning algorithms, and using Python libraries such as NumPy, Pandas, and Scikit-learn. They will also develop the ability to visualize and communicate data insights effectively, making them competitive in the data science job market.

1. 1. Introduction to Data Science with Python: Learners will be introduced to the basics of data science and how to use Python for data manipulation and analysis. They will gain skills in setting up Python environments and working with data structures like lists, dictionaries, and pandas DataFrames.
2. 2. Data Wrangling and Preprocessing: This module covers techniques for cleaning, transforming, and preprocessing data to prepare it for analysis. Learners will learn to handle missing data, perform data normalization, and merge datasets using Python libraries such as pandas.
3. 3. Statistical Foundations for Data Science: Learners will study fundamental statistical concepts and their application in data science. Topics include descriptive statistics, probability distributions, inferential statistics, and hypothesis testing, with practical applications using Python.
4. 4. Data Visualization with Python: This module focuses on creating visual representations of data to facilitate understanding and communication. Learners will master the use of libraries such as Matplotlib and Seaborn for generating various types of plots and charts.
5. 5. Machine Learning Fundamentals: An introduction to the core concepts of machine learning, including supervised and unsupervised learning. Learners will learn to implement basic models like linear regression, decision trees, and K-means clustering using scikit-learn.
6. 6. Advanced Machine Learning Techniques: This module delves into more complex machine learning techniques such as ensemble methods, neural networks, and deep learning. Learners will also explore model evaluation and validation strategies.
7. 7. Data Manipulation with NumPy and SciPy: Learners will gain proficiency in using NumPy for efficient numerical operations and SciPy for scientific and technical computing. Skills include handling multidimensional arrays, performing statistical tests, and solving mathematical problems.
8. 8. Time Series Analysis: This module covers the analysis of time series data, including trends, seasonality, and forecasting. Learners will learn to apply techniques such as ARIMA models and state space models using Python.
9. 9. Text Analytics and Natural Language Processing (NLP): An introduction to processing and analyzing textual data. Learners will learn to clean text data, perform sentiment analysis, and extract insights from unstructured text using libraries like NLTK and SpaCy.
10. 10. Project Management and Data Science Capstone: Learners will apply their skills to a comprehensive project, working in teams to solve a real-world data science problem. This module emphasizes project planning, execution, and the presentation of findings.