Data Science Tutorial

Data Science

Learn scientific methods, processes and algorithms to extract knowledge and insights from structured and unstructured data. Data Science Masters Program makes you proficient in tools and systems used by Data Science Professionals. It includes training on Statistics, Data Science, Python, Apache Spark & Scala, Tensorflow, R and Tableau. So for any student who aspire to become Data Scientist must follow this hireachy to understand the key concept. 

Summary – Learning Path to become a Data Scientist

Here’s a summary of what you can expect to learn (and the steps you should follow) using this learning path:

  • Getting Started with Data Science and Python: The start of your journey to becoming a data scientist! Understand what a data scientist does, the various terms associated with data science, and start getting acquainted with the Python programming language
  • Statistics and Mathematics: The backbone of data science. Some of the key concepts you’ll cover are probability, inferential statistics, and get a hang of how to perform exploratory data analysis (EDA). This will also include the basics of linear algebra (another core machine learning topic)
  • Machine Learning Basics: Welcome to the world of machine learning! This section is all about introducing you to the basic machine learning algorithms and techniques, including linear regression, logistic regression, decision trees, Naive Bayes, support vector machines (SVM), among others
  • Ensemble Learning: Time to deep dive into advanced machine learning topics. Understand what ensembling is, the different ensemble techniques, and start working on datasets to gain a hands-on practical experience
  • Time Series: One of the more complex topics in this space. This deserves an entire section by itself so we have also included a hands-on project to get you familiar with the various time series concepts and how they work in the real-world
  • Matrix Algebra and Recommendation Systems: Why matrix algebra, you ask? Well, you can’t really get serious about learning how recommendation engines work without it! So this section, covered in June in the learning path, is all about these two trending and relevant concepts. This will cover dimensionality reduction techniques like Principal Component Analysis (PCA) along with recommendation engine projects
  • Neural Networks (and Deep Learning): Yes, deep learning is part of the data science learning path. Given the rapid rise and adoption of deep learning applications, this is potentially a very relevant part of your role as a data scientist. You would learn about neural networks and will also pick up a popular deep learning framework called Keras (you can choose others based on your preference – such as PyTorch)
  • Computer Vision: Computer vision is easily the most in-demand deep learning field in the industry. After you’re familiar with the above section, dive into different kinds of computer vision problems and learn as you go
  • Natural Language Processing (NLP): The hottest field in any industry. Businesses are tripping over each other to land the best NLP talent – this is a great time to start working in NLP! From Google’s BERT to Facebook’s RoBERTa, start getting acquainted with the state-of-the-art NLP frameworks

 

 

Statistics for Data Science ------> R for Data Science ------> Python for Data Science ----- > Spark and Scala ------> Tensorflow.

 

Feel free to drop a note or ping if you need any assistance for this course. 

Statistics for Data Science

Statistics for Data Science

Learn Mathematical Science pertaining to data collection, analysis, interpretation and presentation. Solve complex problems to become Data Scientists.

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R for Data Science

R for Data Science

This course will teach you how to do data science with R. You’ll learn how to get your data into R, visualise, transform and model it.

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Python for Data Science

Python for Data Science

Lear the ability to analyze data with Python is critical in data science. Python for Data Science is a must learn for professionals in the Data Analytics domain.

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