40 Weeks
Learn the art of creating data visualizations and how to apply statistics & predictive analytics in a business environment.
This course provides a comprehensive foundation in data science. You’ll start with the basics of programming using Python and gradually progress to working with real datasets, performing data analysis, and building simple machine learning models. Whether you're new to coding or looking to deepen your data skills, this course offers a structured path toward becoming a confident and capable data practitioner. This course is ideal for beginners, career switchers, university graduates, or anyone passionate about building a career in tech.
Why You Should Join
Our Data Science course is designed to equip you with practical, job-ready skills in data analysis, statistics, machine learning, and Python programming. Whether you're new to tech or looking to upgrade your skills, this program offers a structured path to becoming a data-driven professional.
We provide blended learning that integrates state-of-the-art technologies in course delivery, combining online resources, in-person class sessions, and real-world projects.
Somakodi offers some of the most competitive payment options, with a range of flexible payment plans and also offer Scholarships opportunities.
We understand that every individual’s career journey is unique — that’s why our dedicated career coaching team offers one-on-one personalized support to help you navigate your path with confidence. From resume reviews and mock interviews to personal branding and job search strategies, we’re here to equip you with the tools you need to stand out and secure your first role in tech.
What You will Learn
Unit 1: Data Science Foundations
n this module, you’ll be introduced to the core principles and concepts that define the field of data science. You’ll explore the data science lifecycle, learn how to think analytically, and understand how data is used to solve real-world problems across industries. Topics include types of data, the role of a data scientist, the difference between descriptive, predictive, and prescriptive analytics, and ethical considerations in data work. This foundation will give you the context and mindset needed to approach data projects critically and confidently as you progress through the course.
Unit 2: Python for Data Science
In this module, you’ll learn how to use Python, the most popular programming language in data science, to manipulate, analyze, and visualize data. Starting with the basics like variables, data types, and control structures, you’ll quickly progress to working with essential libraries such as NumPy for numerical computing and pandas for data manipulation. You'll also get hands-on experience writing clean, efficient code to load, filter, and transform datasets. By the end of this module, you'll be comfortable using Python to perform practical data tasks and prepare data for deeper analysis.
Unit 3: Data Analysis & Visualization
This module focuses on exploring, cleaning, and interpreting data to uncover meaningful insights — even when working with large and complex datasets. You’ll learn how to handle messy or incomplete data, identify trends, and summarize findings using descriptive statistics. With tools like pandas, matplotlib, and seaborn, you'll build compelling visualizations including bar charts, scatter plots, and heatmaps. You’ll also be introduced to the concept of Big Data, learning how data at scale is managed and analyzed in the industry. By the end of this module, you’ll be able to confidently work with data, generate insights, and communicate your findings through clear, data-driven storytelling.
Unit 4: Statistics & Probability
In this module, you’ll learn how to summarize, interpret, and draw conclusions from data using key statistical concepts like mean, standard deviation, correlation, and probability distributions. You’ll also explore hypothesis testing and confidence intervals to support data-driven decision-making. These skills are essential for building reliable and insightful analyses.
Unit 5: Machine Learning
This module introduces you to the fundamentals of machine learning and how it's used to make predictions from data. You’ll learn the difference between supervised and unsupervised learning, explore key algorithms like linear regression, decision trees, and clustering, and practice building models using the scikit-learn library. By the end, you’ll understand how to train, evaluate, and improve models to solve real-world problems.
Unit 6: Workplace Readiness & Leadership Program
Beyond technical skills, this program prepares you for success in real-world work environments through targeted training in professional development, communication, and leadership. You’ll learn how to collaborate effectively in teams, manage tasks, and communicate your ideas clearly in meetings. The program also includes personal branding, time management, problem-solving, and conflict resolution skills. Through workshops, peer learning, and mentorship, you'll develop the confidence and soft skills needed to thrive not just as a developer, but as a future team leader or tech innovator.
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Learn in-demand skills and launch a new career as a Data Scientist with this intensive bootcamp.
