Description

Data Science is an interdisciplinary field which draws techniques and theories within the context of mathematics, statistics, computer science and information science. It helps understand and analyze actual phenomena from unstructured and raw data, with the help of scientific methods, processes, and algorithms, which would be advantageous in decision making. This course focuses on teaching the students about the components, skills, tools and techniques of the course, like, programming, machine learning, data visualization and more. It also provides detailed knowledge of analytics and evaluation. Moreover, the institution also helps prepare the students for interviews and jobs.

This is an instructor-led course with an average batch size of 10 students. In the 80 hours of Online-Recorded training, you will get both the theoretical and practical knowledge needed to build the necessary skills. The institute’s holistic approach is stemmed to meet the long-term needs of the student and hence they provide 100% job/placement assistance with an option of seeking a trial class before the enrolment. 

What will I learn?

  • Basics of Data Science, Brief Background in Python Or Unix and Jupyter and Numpy
  • You will be working on the Mini Project in data science.
  • You will learn Python programming and the use of python in data science  
  • Statistics, Math, Data Analytics and Machine Learning.

Specifications

  • Free Demo
  • 100% Placement Assistance
  • Life-time Access to Content
  • Certification by Institute
  • Reasonable Fees
  • Value for Money
  • CV Tailoring
  • Aptitude Training

Data Science with Python

  • Introduction to Data Science
  • Brief Background in Python Or Unix
  • Jupyter and Numpy
  • Pandas
  • Visualization
  • Mini Project
  • Machine Learning
  • Working With Text and Databases
  • Final Project

 

Python introduction

  • Installing Pycharm, Pydev, Anaconda, Python

 

Python data types

  • Integer, String
  • List
  • Tuple
  • Set
  • Dictionary
  • Conditions
  • Loop
  • Numpy
  • Pandas
  • What is a Plot?
  • Matplotlib
  • Def (functions)
  • stack and queues

 

Basics of Maths

  • Vectors
  • Magnitude

 

Basics of statistics

  • Mean
  • Median
  • Arithmetic Mean
  • Geometric Mean
  • Harmonic Mean
  • What is Plotting?

 

Basics of Data Analysis

  • Data types
  • Data life cycle
  • Data analysis introduction
  • Diff B/w Data analysis and Data Analytics
  • Diff B/w AI / ML / DL
  • Scientific notation(conversion)
  • Basics of measurements
  • Basics of designing graphs
  • Basics of designing plots
  • Labelling and graphs
  • How to work with format certifications

 

Levels of Data Analysis

  • Descriptive
  • Diagnostic
  • Predictive
  • Prescriptive

 

Machine Learning

  • Basics of ML
  • scikit learn
  • Small example prediction
  • Machine learning Types
  • How does machine learning works
  • Regression and Classifications
  • Classifications
  • IRIS data sets
  • Classification Algorithms
  • Decision Tree

 

Decision Tree

  • Creating a decision tree
  • CART algorithm
  • Decision Tree Terminologies
  • Gini Index, Information Gain
  • Reduction Variance, Chi-Square

 

KNN Algorithm ‘

  • What is KNN?
  • Advantages of KNN
  • Disadvantages of KNN
  • Predicting with Example(ML)

 

Linear Regression

  • Logistic Regression
  • Clustering

 

PROJECT1 (ML)

  • Deep Learning using TensorFlow(Google)
  • Introduction to Deep Learning
  • Introduction to Artificial Neural Networks
  • Introduction to Tensorflow
  • what are Tensors
  • Tensor ranking
  • Types of Tensors
  • Image Classifications with Tensorflow I
  • Image Classifications with Tensorflow II
  • Face reorganization with Tensor flow
  • Activation Functions in a Neural network explained
  • CNN (Convolution Neural Networks) algorithm
  • RNNRecurrent Neural Networks for Language modelling
  • Gated Recurrent Units(GRUs), LSTMs
  • Recursive Neural network

 

Adv Deep Learning

  • NLP
  • NLP Terminology
  • NLP with Deep Learning

 

Project 2

  •  IT Systems Analyst
  • Healthcare Data Analyst
  • Operations Analyst
  • Data Scientist
  • Data Engineer
  • Quantitative Analyst
  • Data Analytics Consultant
  • Digital Marketing Manager
  • Project Manager
  • Transportation Logistics Specialist

 

Data Science Using R Training

  • Data Science Introduction & Use Cases
  • Python Basics: Basic Syntax, Data Structures
  • Python Basics: Loops, If-elif statements, Functions, Exception Handling
  • Statistics, Measures of central tendency, Population, Sample, Probability Distribution, Normal and Binomial Distribution, Random Variable, Pictorial Representations
  • Python Advanced: Numpy, Pandas
  • Python Advanced: Data Manipulation, Matplotlib

 

Machine Learning

  • ML Introduction & Use Cases
  • Statistics 2 – Inferential Statistics
  • Linear Regression
  • Logistic Regression
  • Decision Trees, Random Forest
  • Modelling Techniques (PCA, Feature Engineering)
  • KNN, Naive Bayes
  • Support Vector Machines(SVM)
  • Clustering, K-means

 

Deep Learning With NLP

  • Introduction to NLP & Deep Learning
  • Word Embeddings
  • Word window classification
  • Introduction to Artificial Neural Networks
  • Introduction to Tensorflow
  • Recurrent Neural Networks for Language modelling
  • Gated Recurrent Units(GRUs), LSTMs
  • Recursive Neural network

 

Advanced Machine Learning 

  • Market Basket Analysis & Apriori Algorithm
  • Recommendation System
  • Dimensionality Reduction
  • Anomaly Detection
  • XG Boost
  • Gradient Boosting Machine(GBM)
  • Stochastic Gradient Descent(SGD)
  • Ensemble Learning

 

Data Analytics With R

  • Data Science Introduction & Use Cases
  • R Basics: Basic Syntax, Variable assignment, Data Types-numeric, string, boolean
  • R Basics: Vectors, Matrices, Factors, Data Frames, ListsLoops, If-elif statements, Functions, Exception Handling
  • Statistics, Measures of central tendency, Population, Sample, Probability Distribution, Normal and Binomial Distribution, Random Variable, Pictorial Representations
  • R Advanced: Libraries
  • R Advanced: Data Manipulation, plots
  • Exploratory Data Analysis: Data Cleaning, Data Wrangling

Dr.Prem Kandela

The trainer has 15 years of industry experience and more than 10 years of teaching experience and trained 1000+ students. The trainer is an expert in Data Science, Robotic Process Automation and Python. The trainer has in-depth knowledge in Unix, Jupyter, Numpy, Blue Prism and Ui Path.

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Description

Data Science is an interdisciplinary field which draws techniques and theories within the context of mathematics, statistics, computer science and information science. It helps understand and analyze actual phenomena from unstructured and raw data, with the help of scientific methods, processes, and algorithms, which would be advantageous in decision making. This course focuses on teaching the students about the components, skills, tools and techniques of the course, like, programming, machine learning, data visualization and more. It also provides detailed knowledge of analytics and evaluation. Moreover, the institution also helps prepare the students for interviews and jobs.

This is an instructor-led course with an average batch size of 10 students. In the 80 hours of Online-Recorded training, you will get both the theoretical and practical knowledge needed to build the necessary skills. The institute’s holistic approach is stemmed to meet the long-term needs of the student and hence they provide 100% job/placement assistance with an option of seeking a trial class before the enrolment. 

What will I learn?

  • Basics of Data Science, Brief Background in Python Or Unix and Jupyter and Numpy
  • You will be working on the Mini Project in data science.
  • You will learn Python programming and the use of python in data science  
  • Statistics, Math, Data Analytics and Machine Learning.

Specifications

  • Free Demo
  • 100% Placement Assistance
  • Life-time Access to Content
  • Certification by Institute
  • Reasonable Fees
  • Value for Money
  • CV Tailoring
  • Aptitude Training
₹23,000 ₹ 25,000

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