Overview:

This course seamlessly blends essential Python programming, hands-on data exploration, and practical machine learning concepts for a comprehensive learning experience. Students will explore libraries like Pandas, NumPy, Matplotlib, and scikit-learn to manipulate, understand, and build predictive models with your data, and will learn to leverage generative AI support for code generation, troubleshooting, and concept understanding.

In this course students will focus on:

Module 1: Python and Data Fundamentals

In this module, students will dive into the essentials of Python for data science. They will learn about variables, data types, how to control the flow of their code with conditionals and loops, and how to build modular code using functions. Additionally, they'll be introduced to the cornerstone libraries of data science ??? NumPy for numerical operations and Pandas for working with tabular data in Data Frames. Students will learn how to load datasets, perform basic data cleaning, and transformations. To tie it all together, students will be guided on setting up a Jupyter Notebook, the preferred working environment for data scientists.

Module 2: Exploratory Data Analysis (EDA)

The power of data lies in understanding the story it tells. In this module, students will master the art of Exploratory Data Analysis (EDA). Students will learn techniques for handling missing data and outliers, and how to convert data into appropriate formats. Students will calculate essential summary statistics with NumPy and Pandas, uncovering measures like mean, median, and standard deviation. The focus then shifts to visualization. Students will harness Matplotlib and Seaborn to create histograms, scatterplots, and boxplots, learning to interpret these to glean insights from their data. They will solidify these skills with an EDA mini project, where they'll take a dataset from start to finish.

Module 3: Introduction to Machine Learning

Students will embark on the exciting world of machine learning! This module introduces the fundamentals. They will understand the differences between supervised and unsupervised learning, as well as classification and regression tasks, illustrated with real-world examples. Students will deep dive into linear regression, learning how this model works, implementing it with scikit-learn, and interpreting the results. Next, they will explore decision trees - how they are built, visualized, and understood. Finally, model selection concepts like train/test splits, overfitting, and cross-validation will be introduced.

GENERATIVE AI Support Throughout

Students will have seamless support through integrated GENERATIVE AI assistance. They will get tailored code examples for common operations, help with troubleshooting errors, and easy-to-understand explanations for complex concepts, making their learning journey smoother.

Learning Outcomes:

By the end of this course, the students should be able to: 

• Python Fluency: Demonstrate proficiency in core Python concepts (variables, data types, control flow, functions) for data science tasks.

• Data Handling Expertise: Utilize Pandas to effectively import, clean, transform, and manipulate datasets for analysis and modelling.

• Exploratory Analysis Mastery: Employ NumPy, Matplotlib, and Seaborn to calculate summary statistics and create informative visualizations, extracting meaningful insights from data.

• Machine Learning Foundations: Understand the principles of supervised learning and build basic linear regression and decision tree models using scikit-learn. Evaluate model performance using appropriate metrics.

• Process-Oriented Mindset: Apply a structured workflow to a data science project encompassing data cleaning, exploratory analysis, model selection, and result interpretation.

We provide tailored versions of our courses to cater to the specific needs of organisations. The pricing for customised courses may differ from that of our open-enrollment courses. For more details on pricing and instructor information, kindly reach out to us via email at ces@lums.edu.pk.

Course Outline

Instructor

Farah Nadeem

Faculty member at LUMS and holds a Ph.D. in Electrical and Computer Engineering from the University of Washington, specifically focusing on machine learning and natural language processing. Her research interests include data analytics for education, evidence-informed management and policy-making, and natural language processing for educational applications. She has worked with academic, public, and development sector organizations including the University of Washington, UNICEF, and the World Bank.

Application Process:

Participants must register online through the CES website by clicking on their desired course and filling out a one-page form. Before starting the registration process, please ensure that you have the following: 

- A passport-sized photograph for the CES smart card. It should be against a white background. 

- Correct CNIC details. 

Incorrect information provided during the registration process will lead towards cancellation of your enrolment at any stage, and participants will not be entitled to claim a refund.

Once you have completed your online registration, you may proceed to the payment section. You have two options: 

1. Pay online using your bank debit/credit cards through LUMS online payment system https://pay.lums.edu.pk 

2. Download the fee voucher and deposit cash in any branch of the designated banks specified on the fee voucher. 

Once the payment has been made, you will receive a confirmation email within 24 to 48 working hours, and your payment status will be updated to "ENROLLED" in the online application portal.

Installment:

KalPay Taleem offers an instalment facility for participants enrolled in CES on-campus courses and workshops. If you want this option, please email taleem@kalpayfinancials.com or call at 0328 3044414 for more information.

Note: CES may cancel or postpone a course due to insufficient enrolment or unforeseen circumstances. In this case, the university will refund registration/processing fee (if any) but will not be responsible for any other related charges/expenses including cancellation/change charges by airline and travel agencies.

Please go through Policies & Code of Conduct