Course Description
This curriculum is designed for audience who knows nothing or very little about Data Science or related subject and are interested to take their first step towards learning this vast field.
What You’ll Learn From This Course
- The basics of statistics and probability, including descriptive statistics, probability distributions, hypothesis testing, and regression analysis.
- The fundamentals of programming with Python, including data structures, control structures, functions, and object-oriented programming.
- How to use Python for data manipulation, analysis, and visualization.
- The concept of vectors and how they are used in data science and machine learning, including vector operations, vector spaces, and linear algebra.
- The basics of SQL, including database design, querying, and data manipulation using SQL commands.
- How to use machine learning algorithms such as decision trees, logistic regression, and k-nearest neighbors for classification and regression tasks.
- How to evaluate machine learning models using performance metrics such as accuracy, precision, recall, and F1 score.
- How to build and deploy machine learning models using popular frameworks.
Introduction to Data, Data Science and Statistics
Data Science Fields- Relationship between different Data Science Fields
- Purpose of each Data Science Field
- Common Data Science Techniques
- Common Data Science Tools
- Data Science Jobs: Expectation
- Dispelling common Misconception
Statistics
- Introduction to Statistics
- Descriptive Statistics Fundamentals
- Inferential Statistics Fundamentals
- Confidence Intervals
- Hypothesis Testing
- Case Study
This week begins with the very basics of data science and statistics and builds up your arithmetic thinking. It gradually teaches you how to work with more complex analysis, statistical approaches, and hypothesis.
Probability
- Basics of Probability
- Combinatorics
- Bayesian Inference
- Discrete Distributions
- Continuous Distributions
- Probability in Other Fields
- Hands-on Assignment
- Case Study
Mathematics
- Matrix
- Scalars
- Vectors
- Linear Algebra and Geometry
- Eigen Values and Eigen Vector
- Dimensionality Reduction
- Principal Component Analysis
- Linear Discriminant Analysis
SQL
- Introduction
- DDL
- DML
- Aggregates
- Joins
- Subqueries
- Best Practices
- Hands-on assignment
This module will teach you the mathematical fundamentals used in the fields of data science by covering topics of calculus and linear algebra. It also delivers the numerical tools required for applying mathematical knowledge into practice. Also, SQL is a must if you are expected to work with databases. This module is a guide, teaching you everything you need to know in terms of database management and creating SQL queries as a Data Scientist.
Python
- Python Basics
- Introduction to Jupyter Notebook
- Variables and Strings
- Conditions
- Loops
- Dictionaries
- Classes
- Optimized programming for Data Science
- Visualization
- Pandas
- Hands-on Lab
The module is the ideal tool to further improve your Python skills. The course will guide you through some of the basics, but also introduce some advanced techniques used with this programming language.
Business Analytics
- Understanding Business
- End-to-End processes in an organization
- Targets
- Maturity Stages in Analytics
- Analytics Techniques in Practice
- Analytics Lifecycle
- Data Visualization for Business Analytics
- Hands-on Analysis
- Case Study
Do you want to know how worldwide top business corporations build and use data-based decision-making systems to succeed? This module will guide you through the business analytics fundamentals and importance of data to make informed decisions.
Machine Learning
- Exploratory Data Analysis
- Data Preprocessing and Feature Engineering
- Linear Regression and Implementation
- Logistic Regression
- Cluster Analysis
- K-Means Clustering
- Decision Trees & Random Forest
- Naïve Bayes
- SVM
- Business Cases Studies
- Introduction to Deep Learning
- Project Assignments
- Hands-on Labs
- Project Kick-Offs
These weeks are focused on predictive modelling via an array of approaches such as linear regression, logistic regression, and cluster analysis. It combines comprehensive theory with lots of practice to allow you to exercise your Python skills.
Group Discussion
- Peer Reviews
- Consulting Basics
Faraz Shahid
SOFTWARE ARCHITECT - DATA SCIENCE AT NORTHBAY SOLUTIONS
A Data Science Consultant with over 13 years of experience in successfully delivering analytics and information systems solutions/projects to leading telecommunications, retail, FMCG and banks across Pakistani, MEA and North American markets. He has led the successful inception, design and execution of advanced analytics, business operations and managed services projects covering different industries. His recent experience includes delivering use cases like market basket analysis, people analytics, performance prediction, customer experience and path analytics. He has also held technical lead roles on several data science and managed services projects in a multi-platform ecosystem environment.
Usman Shahrukh
SENIOR DATA SCIENTIST AT JAZZ
He has 11 Years of experience in the domain of Data Science and Business Analytics. He has worked in Telecommunication, Pharmaceutical, Banking, Retail industry and have implemented Data Science use cases in Pakistan, Sweden and Turkey. He has also worked in Commercial Analytics in USA and as a Business Intelligence Consultant in Sweden and Switzerland.
Duration
Timings
Hours
Instructor
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