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Certified Data Science and Analyst Program | CDSAP

Created By: Training Head

5 (15 Ratings) • 16 Students Enrolled
What you'll learn
  • Foundations of Data Science
  • Data Manipulation and Cleaning
  • Exploratory Data Analysis
  • Statistical Analysis with Python
  • Machine Learning Fundamentals
  • Supervised Learning Algorithms
  • Unsupervised Learning Algorithms
  • Advanced Topics
  • Capstone Project

Prerequisites: Basic Excel, SQL, and Python skills. No prior data science knowledge required. Ideal for beginners seeking a foundation in data science and machine learning.

Course Description

Embark on a transformative journey into the realm of data with our 'Introduction to Data Science (ML & DL)' course. Discover the foundations of data science, delve into Python programming for data manipulation, and explore machine learning fundamentals. Gain proficiency in advanced topics such as feature engineering, deep learning, and model deployment. Cap off your learning with a hands-on Capstone Project, applying your skills to real-world challenges. Elevate your career opportunities as a Machine Learning Engineer, Data Scientist, or Research Scientist. No prior knowledge needed. Join us for a comprehensive learning experience and unlock the potential of data analysis!

Course Content


Data Science

Data Science - 1

Data Science - 2

Data Science - 3

Data Science - 4

Data Science - 5

Data Science - 6

Data Science - 7

Data Science - 8

Data Science - 9

Data Science - 10

Course Reviews

Pedestal Techno World Private Limited offers an exceptional learning experience, merging industry insights and expert guidance for skillful futures.

5 Based on 15 Reviews
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The Advanced Topics segment was intriguing. Feature engineering and deep learning were challenging but rewarding.

Rahul Kapoor

The Capstone Data Analysis Project was a perfect way to culminate the course. It allowed me to showcase my skills and build a portfolio for future opportunities.

Kavya Choudhary

Advanced Data Analysis Techniques were insightful. More real-world examples would have made it even better.

Harish Tiwari

Statistical Analysis was challenging but rewarding. The instructors' emphasis on practical applications helped me grasp the concepts better.

Nandini Mehta

The section on Data Cleaning and Wrangling provided practical skills that I immediately applied to my own datasets.

Siddharth Desai

Data Visualization was well-taught. The visualizations created during the course were both aesthetically pleasing and informative.

Sneha Reddy

Introduction to Data Analysis was a good refresher. It laid a solid groundwork for the more complex topics that followed.

Vikram Malhotra

The Capstone Project was the highlight for me. Applying everything learned in a real-world scenario was both exciting and educational.

Aisha Joshi

Unsupervised Learning Algorithms were explained with clarity. The practical applications and case studies made the concepts more tangible.

Arjun Khanna

The Supervised Learning Algorithms section felt a bit rushed. More in-depth coverage and additional exercises could have enhanced the learning experience.

Pooja Gupta

The Machine Learning Fundamentals module was extensive, covering various algorithms. The practical examples and group projects were beneficial.

Rajesh Kumar

Statistical Analysis with Python was comprehensive. The real-world examples used made it easier to apply statistical concepts to actual problems.

Ananya Verma

The Exploratory Data Analysis section was my favorite. It really helped me gain confidence in working with diverse datasets, and the instructors were very supportive.

Rohit Singh

Data Manipulation and Cleaning segment was thorough. The hands-on exercises were challenging but immensely helpful in improving my Python skills.

Priya Sharma

The Foundations of Data Science module provided a clear and solid introduction. The instructors were engaging, making complex concepts easy to understand.

Akshay Patel
This Course Includes:

Duration : 9 Months

Letures : 108

Offline & Online Mode