Data Science & ML With Python Training & Certification in Pune
Learn Data Science, Deep Learning & Machine Learning with Python .
Live Machine Learning & Deep Learning Projects
Duration : Duration : 60 Hrs | 2 Major Projects | 10 Minor Projects | 100 + Assignments
Data Sets , installations , Interview Preparations , Repeat the session until 6 months are all attractions of this particular course
Trainer :- Experienced DataScience Consultant
Want to be Future Data Scientist
Introduction: This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median mode etc. and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualizing your findings. If you’re a programmer or a fresh graduate looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic to Advance techniques used by real-world industry data scientists.
Data Science, Statistics with Python This course Start with introduction to Data Science and Statistics using Python. It covers both the aspects of Statistical concepts and the practical implementation using Python. If you’re new to Python, don’t worry – the course starts with a crash course to teach you all basic programming concepts. If you’ve done some programming before or you are new in Programming, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC’s; the sample code will also run on MacOS or Linux desktop systems.
Analytics: Using Spark and Scala you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Data frames to manipulate data with ease.
Machine Learning and Data Science : Spark’s core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We’ll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets.
Real life examples: Every concept is explained with the help of examples, case studies and source code wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant. finance context.
Engineering/Management Graduate or Post-graduate Fresher Students who want to make their career in the Data Science Industry or want to be future Data Scientists.
Engineers who want to use a distributed computing engine for batch or stream processing or both
Analysts who want to leverage Spark for analyzing interesting datasets
Data Scientists who want a single engine for analyzing and modelling data
MBA Graduates or business professionals who are looking to move to a heavily quantitative role.
Engineering Graduate/Professionals who want to understand basic statistics and lay a foundation for a career in Data Science
Working Professional or Fresh Graduate who have mostly worked in Descriptive analytics or not work anywhere and want to make the shift to being data scientists
Professionals who’ve worked mostly with tools like Excel and want to learn how to use Python for statistical analysis.
Introduction to Data Science with Python
What is analytics & Data Science?
Common Terms in Analytics
Analytics vs. Data warehousing, OLAP, MIS Reporting
Relevance in industry and need of the hour
Types of problems and business objectives in various industries
How leading companies are harnessing the power of analytics?
Critical success drivers
Overview of analytics tools & their popularity
Analytics Methodology & problem solving framework
List of steps in Analytics projects
Identify the most appropriate solution design for the given problem statement
Project plan for Analytics project & key milestones based on effort estimates