Data Science- Professional Course

Hone practical skills in Python, data analysis, visualization, statistics, and machine learning with Cronus Consultants’ professional Data Science course. In this 6-month, 100-hour program, learn the complete data science workflow, from Python programming and data preparation to exploratory analysis, statistical techniques, machine learning, model evaluation, deployment, and hands-on project execution.

6 Months
Online / offline

Why Choose Cronus Consultants to Learn Data Science?

At Cronus Consultants, learners are introduced to the complete data science process, from understanding raw datasets and preparing data to discovering insights, building machine learning models, and deploying solutions. The curriculum combines Python programming, data visualization, statistics, and machine learning with practical exercises and project-based learning.

Curriculum Overview

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Understand the fundamentals of Data Science, its role in modern analytics, and the stages of the Data Science Lifecycle while exploring the differences between Data Science, Data Analytics, Artificial Intelligence, and Machine Learning.
Explore key industry roles, including Data Scientist, Data Engineer, and Machine Learning Engineer, and set up the data science environment using Jupyter Notebook, Anaconda, and Python.

Learn Python fundamentals, including variables, data types, conditional statements, loops, and essential data structures such as Lists, Tuples, Dictionaries, and Sets.
Understand functions, modules, and commonly used Python libraries while applying programming concepts through hands-on exercises and basic problem-solving tasks.

Learn to prepare, clean, and transform datasets using NumPy arrays and Pandas Series/DataFrames, including indexing, reshaping, array operations, and efficient data manipulation.
Perform essential data-cleaning and manipulation techniques such as handling missing values, removing duplicates, converting data types, merging, joining, concatenating, GroupBy operations, and working with CSV files.

Learn descriptive statistics such as mean, median, mode, variance, and standard deviation, and apply EDA techniques to identify patterns, trends, and relationships in datasets.
Create effective visualizations using Matplotlib and Seaborn, including line plots, bar charts, heatmaps, pair plots, and box plots, using sample datasets such as Titanic and Housing data.

Understand fundamental probability concepts and distributions, including Normal, Bernoulli, and Binomial distributions, along with the principles of inferential statistics and hypothesis testing.
Learn about p-values and apply statistical tests such as Z-tests and T-tests while understanding the difference between correlation and causation and the importance of careful statistical interpretation.

Understand Machine Learning Fundamentals, Including Supervised And unsupervised learning, and explore regression and classification techniques.
Learn model evaluation techniques using Accuracy, Precision, Recall, and Confusion Matrix, and build and evaluate a practical prediction model using scenarios such as customer churn or stock-price prediction.

Understand the end-to-end data science workflow, including defining a business or analytical problem, preparing data, performing EDA, developing models, and evaluating results.
Learn the fundamentals of model deployment and model storage using Pickle, and apply the complete workflow through a short capstone project.

Apply the complete data science pipeline through an end-to-end practical project covering problem definition, data preparation, EDA, visualization, model development, evaluation, and basic deployment.
Gain practical experience in Python-based analysis, machine learning, model storage, deployment concepts, cloud storage, project execution, and final project demonstration.

Key Features

Python Programming

Build Python skills for data science

Data Analysis

Clean, transform, and analyze real-world data

Data Visualization

Visualize data and uncover meaningful insights

Machine Learning

Master core ML algorithms and model evaluation

Who can pursue this course?

BBA B.A. B.Sc(Computer Science) M.Sc (Computer Science) B.TECH M.TECH Commerce MBA

      
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 Trustindex verifies that the original source of the review is Google. It was wonderful experience in my first industrial visit. I got to learn a lot about how things work in industry.
  
 
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 Trustindex verifies that the original source of the review is Google. The experience was good, learned a lot.
  
 
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 Trustindex verifies that the original source of the review is Google. It was a good experience of my first industrial training and heard about all of new things
  
 
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 Trustindex verifies that the original source of the review is Google. Experienced a student seminar . Learnt loads and had an industry insight . Thanks to the mentors and professionals for giving us such a good seminar