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 on 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-Live 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
  • Missed Class Recovery
  • Certification by Institute
  • Instalment Facility
  • Interview Training

Data Science with Python and Julia

  • Python introduction
  • Installing Pycharm, Pydev, Anaconda, Python,
  • Python datatypes
  •  Integer, String
  •  List 
  •  Tuple
  •  Set
  •  Dictionary
  • Run time Values: input() / raw_input()
  • Conditions (if, simple if, chained conditions etc)
  • Python strings (len / strip /range /
  • Formatting outputs
  • Loops (while, for loop)
  • Installing packages(pip / conda)
  • Numpy (Numpy Operations)
  • Pandas (Pandas Operations, data frames)
  • What is a Plot?
  • Matplotlib
  • Def (functions)
  • Classes (__init__ , self, __ del_ )
  • __main__
  • Python OOPS
  • Super
  • Stack and queues
  • Basics of Julia
  • Installing a Julia Working Environment
  • Working with Variables and data Types
  • Controlling the Flow
  • Diving Deeper Into Julia
  • Using Types and Parameterized Methods
  • Optimizing Code by Using and Writing Macros
  • Code in Modules
  • Working with the Package Ecosystem
  • Working With Data In Julia
  • Reading and Writing Data Files and Julia Data
  • Using DataArrays and DataFrames
  • The Power of DataFrames
  • Basics Statistics With Julia
  • Exploring and Understanding a Dataset Statistically
  • Overview of the Plotting Techniques in Julia
  • Visualizing Data with Scatterplots, Histograms, and Box Plots
  • Basics of Math’s
  • Addition subtraction in Algebra
  • Addition subtraction multiple terms
  • The invisible 1
  • Multiplication and Division of negative numbers
  • Division Negative Numbers
  • Multiplication and Division in Algebra
  • Probability
  • Principles of Probability
  • Bayes Theorem
  • Sigma Notation
  • Delta
  • Gamma
  • Vectors
  • Magnitude
  • Basics of statistics
  • Descriptive Statistics
  • Inferential Statistics
  • Average, Mode
  • SD. Corrélation
  • Mean, Mode
  • Médian, Skewness
  • Variance
  • Standard Deviation
  • Covariance
  • Correlation
  • Regression
  • Anova
  • R-square
  • F test, WOE, VIF
  • Hypothesis Testing
  • Research Hypothesis
  • Statistical Hypothesis
  • Substantive Hypothesis
  • Basics of Data Analysis
  • Types of Data
  • 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
  • Use case of Cadbury chocolate?
  • Why are sales down
  • Descriptive
  • Diagnostic
  • Predictive
  • Prescriptive
  • Normalisation
  • st Level, 2nd Level, 3rd Level, BCNF

 

Artificial Intelligence

  • What is Artificial Intelligence?
  • Founders of A.I
  • History of A.I
  • 7 Aspects of A.I
  • STRONG A.I
  • WEAK A.I
  • A.I as a Threat?
  • A.I Use cases and Applications
  • SINGULARITY
  • I.Q Levels
  • Basics of Hadoop/ Big data
  • Evolution of data
  • What is this big data?
  • Hadoop as a solution
  • Hadoop Ecosystem
  • HDFS and its Architecture
  • Data ware Housing Basics(ETL: Extract, Transform, Load)
  • What is the Need of BI ?
  • What is Data warehousing
  •  Key Terminologies
  •  OLTP Vs OLAP
  •  ETL
  •  DATA MART
  •  TYPES OF DATAMART’s
  •  METADATA
  • Creating a DataMart
  • SQL Basics
  • CRUD
  • DDL
  • DML
  • DCL
  • TCL
  • DQL
  • Create/select/update/alter/delete/truncate/rollback/commit etc
  • Aggregate Functions(SUM/MIN/MAX/AVG)
  • Basic Queries
  • Machine Learning
  •  Basics of ML
  • SCIKIT learn
  • Small example prediction
  • Machine learning Types
  • How does machine Learning works

 

Types of Machine Learning

  • Supervised
  • Unsupervised
  • Reinforcement
  • Over fitting and Underfitting
  • Bias and Variance

 

What is Regression?

  • What is Classification?
  • Classification Algorithms?
  • Linear Regression
  • Linear Formula, R-square, adjusted R-square
  • How to calculate mean / mean square
  • Designing Regression Line (using Maths and Stats)
  • Algorithm and use case (Practical Prediction)
  • Logistic Regression
  • What is Logistic Regression?
  • Linear vs Logistic Regression
  • Data wrangling
  • Algorithm
  • Use with Prediction (Practical)
  • Decision Tree
  • Creating decision tree
  •  CART algorithm
  •  Decision Tree Terminologies
  •  Gini Index, Information Gain
  •  Reduction Variance, Chi Square
  •  How to choose best Attribute?
  •  Entropy?
  •  What is impurity?
  •  How to build Decision tree
  •  How to build a Root node, Leaf node
  •  What is Pruning?
  •  Practical Example on Decision Tree (ML)
  •  Random Forests
  •  Working with Random forests Practical example (Prediction)
  • Naive Bayes
  •  Bayes Theorem
  •  Loading datasets
  •  Practical example (Prediction)
  •  SVM (Support Vector Machine)
  •  What is a Hyperplane?
  •  How to load datasets
  •  Practical example on SVM (ML)
  •  KNN Algorithm’
  •  What is KNN?
  •  Advantages of KNN
  •  Disadvantages of KNN
  • Predicting with Example (ML)
  • K means Clustering
  • What is Clustering?
  • Diff b/w Clustering and Classification
  • What is centroid?
  • What is Euclidean Distance?
  • Types of Clustering
  •  How K means Clustering works?
  •  Use case with prediction (Practical)

 

Deep Learning

  • Introduction to Deep Learning
  • Why do we need deep learning?
  • Applications of Deep learning
  • What is a Neural Network?
  • What is Biological neural?
  • Introduction to Artificial Neural Networks
  • Difference b/w Biological neural and Artificial Neural Network
  • Work with Activation Functions (Sigmoid, Threshold, ReLU, and Hyperbolic)
  • Introduction to Tensor flow
  • What are Tensors?
  • Tensor ranking
  • Types of Tensors
  • Image Classifications with Tensorflow I
  • Image Classifications with Tensorflow II
  • Face recognition with Tensor flow
  • CNN (Convolutional Neural Networks) algorithm
  • Practical use case
  • RNN (Recurrent Neural Networks) algorithm
  • Practical Use case
  •  Project 1 (Machine Learning)
  •  Project 2 (Deep Learning
  •  Project 3 (live project)

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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Batch Start Date End Date Timings Batch Type
Batch 1 07-06-2021 13-08-2021 Mon-Fri 10:00 AM-12:00 PM Weekday

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 on 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-Live 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
  • Missed Class Recovery
  • Certification by Institute
  • Instalment Facility
  • Interview Training
₹25,000 ₹ 30,000

Hurry up!! Limited seats only

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