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GUVI’s Data Science Course is a top-tier tech course designed to set you up for a high-paying career, and it is certified by IIT-M Pravartak, taught by experienced industry professionals, including former PayPal employees and With expert mentorship from leading data specialists, this Data Science Course not only sharpens your skills but also connects you with Fortune 500 companies, and the goal is to help top organisations find the right talent—and that could be you!
Learn from India’s top Industry Leaders
Course Syllabus (Data Science Course)
This course has been designed to make you aware of real-world data science skills through hands-on projects. You’ll work with top technologies like Python, MongoDB, Pandas, NumPy, Tableau, and Power BI, and you gain practical experience every step of the way. Join this growing community of emerging data scientists who share their expertise to uncover trends and manage data like true analytics pros. It has a total of 23 modules, which are given below.
Module 1 : Python – Advanced
Since they have the essential basics of python and you will see some advanced concepts like Comprehensions, File handling, Regular Expressions, Object-oriented Programming, Pickling and many more essential concepts.
Module 2 : Algorithmic thinking with Python
You will explore the need for Algorithmic Thinking and the necessity of efficient coding, They will drive through Data Structures and Algorithms along with Memory Management Techniques
Module 3 : Data handling in Python – Pandas & MongoDB
Since You need to handle huge amounts of data, you will be implementing data handling techniques with Pandas library. And you will explore the different miscellaneous functions of Pandas library in detail.
Module 4 : SQL (Data Science Course)
You will drive into the SQL-based database. You will learn the basics of SQL queries, schemas and normalization.
Module 5 : SQL – Continued (Data Science Course)
You will continue into the SQL-based database. You will learn the SQL Advanced queries, join, Date and Time Functions and Sub Queries.
Module 6 : Probability and Statistics with Numpy
You will go through Probability and Statistics whereas they are key to understand, process and interpret the vast amount of data, You will deal with the basics of probability and statistics like Probability theory, Bayes theorem, distributions etc and their importance. Besides that, You will do hands-on with Numpy upon those concepts
Module 7: Probability and Statistics with Numpy – Continued
You will continue with statistics and probability and You will deal with descriptive and inferential statistics along with Hypothesis testing and a lot of other relevant statistics methods
Module 8: Data Visualisation in Python (Matplotlib/Seaborn/Plotly)
Data Visualization is used to understand data in a visual context so the patterns, trends and correlations in the data can be understood. You will do a lot of visualization with libraries like Seaborn, Matplotlib etc in turn that leads to effective storytelling.
Module 9: Data Engineering with Python
It is always needed to analyze the data and preprocess it, since the real world data is not always industry ready, so in this week You will be dealing with a lot of data cleaning and Exploratory data Analysis techniques which is a very crucial stage for any data science project.
Module 10: Exploratory Data Analysis with Python
Real world data is always messy and it’s very important to understand the statistical nature of data. Exploratory Data Analysis process, involving the preliminary examination of data to understand its characteristics, uncover patterns and identify potential insights.
Module 11: Machine Learning with Sklearn
You are going to explore the need of machine learning and its types, Algorithms when to use and how to use essential mathematical intuition along with Evaluation metrics. You will see in detail about regression algorithms.
Module 12: Machine Learning with Sklearn
In continuation to the ML algorithms we are going to see in detail about different classification algorithms along with mathematical intuition and evaluation metrics
Module 13: Machine Learning with Sklearn – Continued
You are going to explore classification algorithms like tree based algorithms in detail like how to interpret trees, pruning and ensemble methods like bragging and boosting etc
Module 14: Machine Learning with Sklearn – Continued
After dealing with a lot of Supervised machine learning algorithms You will compare and get to know when to use what, Besides that You will deal with the do’s and don’ts while training an ML model.
Module 15: Machine Learning with Sklearn – Continued
Now You will explore Unsupervised learning algorithms, why unsupervised ?, when to use it and as well as the essential mathematical intuition
Module 16 : Deep learning (Data Science Course)
As You move on to more complex problems, such as object recognition and text analysis, our data becomes extremely high dimensional, and the relationship becomes nonlinear. To accommodate this complexity, You move on to building more complex models that resemble our brain.
Module 17: Deep learning with PyTorch
Now that You have a better theoretical understanding of deep learning models, You will spend this module implementing some of these algorithms in PyTorch
Module 18: Deep Learning with PyTorch continued
Now that You have the basic understanding of PyTorch, You will now dive into discussing the implementation details of a few state-of-the-art deep learning architectures in PyTorch
Module 19: Natural Language Processing
You are going to explore Natural Language Processing (NLP). Given the fact that we have a decent understanding of Machine Learning and Deep Learning, You can now explore the powerful ways to handle the NLP use cases
Module 20: Computer Vision ((Data Science Course)
Having a basic understanding of NLP use cases, now You will dive into the CoPputer Vision FundaPentals. You will discuss state-of-the-art CV problePs and their solutions with deep learning.
Module 21: Model Deployment in AWS Cloud Platform
Having a good understanding of ML, DL and various use cases, You will now discuss the platforms through which You can securely deploy these powerful models on production level. More specifically; You will discuss the fundamentals of AWS services and how to use them efficiently
Module 22: Putting it together – Solving DS problems
This whole week You are going to work on industry projects which are currently in demand in the guidance of industry experts
Module 23: Mock Interviews ((Data Science Course)
Eventually, it’s time to attend the mock interviews which will be conducted by the industry experts like Data scientists, IIT professors and renowned HR’s inorder to mould you in every area possible
Who Can take this Course ?
Any aspirant with a minimum High School qualification and an interest in Data Science is eligible for this course. Guvi experts would gladly take it forward.
What is the Course Duration?
Data Science is a course of 3 months with weekday classes & 5 months with weekend live classes
How do i complete this course?
This is a project-based curriculum, every student has to work & submit the projects with our Industry experts’ guidance. MCQs & assessments help evaluate your performance. After the successful completion of the course, GUVI will grant a globally recognized course completion certificate.
What is the Format of the Course?
This Data Science program is offered as Weekend/Weekday Live classes. They also provide the recordings of the sessions.
What is the project submission Format?
You can submit the projects either Online or Offline.
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