Data Analysis, Machine Learning & Deep Learning


This course is an introduction to Data Science and Statistics using the R programming script with Python. It covers both the Statistical thoughts and the practical execution using R and Python. This program will teach you how to organize Microsoft Windows-based PC’s; the illustrative code will continue running on MacOS graphical operating system or Linux open source software operating system.

Target audience:
  • Big Data and Data Science professionals, Software developers
  • Business Intelligence professionals, Information Architects, Project Managers
  • Those looking to make a career in Big Data, Data Science
  • Graduate or Post-graduate management/engineering Fresher Students who need a career in Data Science Industry or should be future Data Scientist.
  • Masters who need to use a scattered enrolling engine for gathering or stream planning or both
  • Specialists who need to utilize Spark for separating datasets
  • Data Scientists who require a single engine for dissecting and exhibiting data.
  • MBA Graduates or business specialists who are wants to move to a quantitative part. Engineers/Professionals who need to know fundamental knowledge and set up a system for a career in Data Science

Course Content:


  • Arrays and Matrices, ND-array object, Array indexing, Data Types, Array math Broadcasting, Std Deviation, Conditional Prob, Covariance and Correlation.Hands-on Exercise – Import numpy module, Create an array using ND-array, Calculate std deviation on an array of numbers, Calculate correlation between two variables
  • Builds on top of NumPy, SciPy and its characteristics, sub packages: cluster, fftpack, linalg, signal, integrate, optimize, stats; Bayes Theorem using SciPyHands-on Exercise – Import SciPy, Apply Bayes theorem using SciPy on the given dataset
  • Plotting Graphs and Charts (Line, Pie, Bar, Scatter, Histogram, 3-D); Subplots; The Matplotlib APIHands-on Exercise – Plot Line, Pie, Scatter, Histogram and other charts using Matplotlib
  • Data frames, NumPy array to a data frame; Import Data (csv, json, excel, sql database); Data operations: View, Select, Filter, Sort, Groupby, Cleaning, Join/Combine, Handling Missing Values; Introduction to Machine Learning(ML); Linear Regression; Time SeriesHands-on Exercise – Import Pandas, Use it to import data from a json file,,Select records by a group and apply filter on top of that, View the records, Perform Linear Regression analysis, Create a Time Series
  • Introduction to Natural Language Processing (NLP); NLP approach for Text Data; Environment Setup (Jupyter Notebook); Sentence Analysis; ML Algorithms in Scikit-Learn; What is Bag of Words Model; Feature Extraction from Text; Model Training; Search Grid; Multiple Parameters; Build a PipelineHands-on Exercise – Setup Jupyter Notebook environment, Load a dataset in Jupyter, Use algorithm in Scikit-Learn package to perform ML techniques, Train a model Create a search grid
  • What is Web Scraping; Web Scraping Libraries (Beautifulsoup, Scrapy); Installation of Beautifulsoup; Install lxml Python Parser; Making a Soup Object using an input html; Navigating Py Objects in the Soup Tree; Searching the Tree; Output Print; Parsing Full or Partial

 Hands-on Exercise – Install Beautifulsoup and lxml Python parser, Make a Soup object using an input html file, Navigate Py objects in the soup tree, Search tree, Print output


  • Introduction to Deep Learning within machine learning, how it differs from all others methods of machine learning, training the system with training data, supervised and unsupervised learning, classification and regression supervised learning, clustering and association unsupervised learning, the algorithms used in these types of learning.
  • Introduction to TensorFlow open source software library for designing, building and training Deep Learning models, Python Library behind TensorFlow, Tensor Processing Unit (TPU) programmable AI accelerator by Google.
  • Mapping the human mind with Deep Neural Networks, the various building block of Artificial Neural Networks, the architecture of DNN, its building blocks, the concept of reinforcement learning in DNN, the various parameters, layers, activation functions and optimization algorithms in DNN.
  • Introduction to GPUs and how they differ from CPUs, the importance of GPUs in training Deep Learning Networks, the forward pass and backward pass training technique, the GPU constituent with simpler core and concurrent hardware


  • Introduction to Hadoop
  • Detailed MapReduce, and HDFS
  • Hive, Pig, Sqoop, Flume, HBase
  • Real-time analytics with Spark
  • Prediction & analysis through clustering
  • Deploying recommender system
  • SAS advanced analytics & R programming
  • Linear and logistic regression
  • Designing and Developing NoSQL applications
  • Mastering Artificial Intelligence Algorithms and their practical use cases
  • Tackle R and R collection to read, procedure and evoke data
  • Grasp horizontal lapse and use it hopefully to build a mass models
  • Grasp the complexities of all the particular data structures in R
  • Use Linear backslide in R to crush the inconveniences in Excel
  • Draw inductions from data and support them using trial of significance
  • Use statistics to play out an energetic examination of a couple of data and present results
  • Use Spark for a combination of analytics and Machine Learning endeavors
  • Understand practical programming creates in Scala
  • Execute complex estimations like PageRank or Music Recommendations Exposure to datasets from Airline deferrals to Twitter, Web graphs, Social frameworks and Product Ratings
  • Use all the features and libraries of Spark: RDDs, Data frames, Spark SQL, MLlib, Spark Streaming and Graph
  • Create code in Scala REPL conditions to produce Scala applications with an IDE
  • Course Completion Certificate.


  1. Learn about the basics and installation of Python
  2. Designing and Developing NoSQL applications
  3. Get to know the Machine Learning Algorithms in Python
  4. Work on a real life Hadoop project running on Python
  5. Use Linear backslide in R to crush the inconveniences in Excel
  6. Understand SQLite in Python, functions, operations and class defining
  7. Python Course Completion Certificate from Blue ocean learning.

What are the various modes of training that SAN IT offers?

If you have any queries we provide email support and solution to queries. You can raise your queries even after completion of training to get support and assistance.

Do you provide placement assistance?

Yes, SAN IT does provide you with placement assistance and the process of preparing yourself for the interview and the job.

What kind of projects will I be working on?

We provide real-world projects wherein you can apply your knowledge and skills that you acquired through our training. Making you perfectly industry-ready.


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