This module equips you with the skills to identify and address biases in data analysis and utilizes the Python for Data Science (Pandas) library for efficient data processing, setting a solid foundation for your data science journey. In particular, you will learn about summarizing and interpreting large amounts of data using descriptive statistics to understand measures of centrality and variability in datasets. Furthermore, you’ll delve into effective data design techniques, incorporating sampling strategies and survey designs, to minimize bias in data science projects, complemented by practical case studies. Finally, you will be introduced to Pandas. You’ll learn how to read data into DataFrame structures, how to query these structures, and how to index, merge, and group data effectively. 

This module consists of one live session with our Research Assistant, one data assignment and one session with the instructor. The live tutorial will provide you with hands-on experience under the guidance of our Research Assistant. During this session the research assistant will conduct a programming demonstration. You will get a chance to interact with the course team and your peers through this live online session. The data assignment in this module is meant to reinforce practice and evaluate understanding of key programming concepts. The live session with the instructor at the end will aim to answer any course-related questions you might have, under the guidance of Dr. Ihsan Qazi. 

In this module, you’ll explore the essentials of Data Cleaning, Exploratory Data Analysis (EDA), Data Visualization, Text Analysis and Relational Databases. You will learn how to prepare and examine data to reveal hidden insights. You will learn about the principles of data visualization and how to articulate data narratives visually. You’ll also advance your skills with data transformations and text analysis. Finally, you will be introduced to basics of relational databases and the Structured Query Language (SQL), focusing on schemas, joins, and sampling. This streamlined journey equips you with crucial skills for insightful data analysis and management. 

This module consists of one live session and one data assignment. The live tutorial will provide you with hands-on experience under the guidance of our Research Assistant. During this session the research assistant will conduct a programming demonstration. You will get a chance to interact with the course team and your peers through this live online session. The data assignment in this module is meant to reinforce practice and evaluate understanding of key programming concepts. 

This module offers a thorough introduction to data analysis techniques and research methodologies for making informed, data-driven decisions. In particular, you will learn about Causal Inference and Statistical Inference to uncover causal relationships between variables and assess the reliability of data. You will learn about randomized control trials, quasi-experimental methods, and evidence-based policymaking. You will learn about hypothesis testing, p-values, and model assessment. Practical exercises on A/B testing and bootstrap sampling will enhance your ability to compare data confidently. Additionally, you will grasp the importance of sample size and power in data studies.

This module also contains one session with the industry expert where you will discover how Data Science is transforming various industries and is shaping future trends. You will also gain practical insights into how Data Science drives real-world solutions. The live session with the instructor at the end will aim to answer any course-related questions you might have, under the guidance of Dr. Ihsan Qazi.

This module consists of one live session with our Research Assistant and one data assignment. The live tutorial will provide you with hands-on experience under the guidance of our Research Assistant. During this session the research assistant will conduct a programming demonstration. You will get a chance to interact with the course team and your peers through this live online session. The data assignment in this module is meant to reinforce practice and evaluate understanding of key programming concepts.

In this module, you’ll embark on a journey through the fundamentals of Machine Learning and Predictive Analytics, starting with an understanding of what models are and diving into the modeling process and loss functions. You will explore regression techniques, from simple to multiple linear regression, and learn how to interpret models while understanding their accuracy through several metrics. Finally, you will delve into classification methods, focusing on logistic regression and the classification metrics. 

This module consists of one live session with our Research Assistant, one data assignment and one session with the instructor. The live tutorial will provide you with hands-on experience under the guidance of our Research Assistant. During this session the research assistant will conduct a programming demonstration. You will get a chance to interact with the course team and your peers through this live online session. The data assignment in this module is meant to reinforce practice and evaluate understanding of key programming concepts. The live session with the instructor at the end will aim to answer any course-related questions you might have, under the guidance of Dr. Ihsan Qazi. 

In this module, you will learn about Big Data, starting with its sources and the pivotal role of distributed file systems in managing it. You will be introduced to the essence of MapReduce data processing framework, with a special focus on Apache Spark, to perform large-scale data processing efficiently. In addition, you will learn about the importance of ethics in data science. You will explore how ethical considerations are integral throughout the data science lifecycle, and the pursuit of fairness in machine learning. This module equips you with a deep understanding of both the technical and ethical foundations necessary for responsible and effective data science practice.