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Data Science Course and Certification Training Program

Diploma in Data Science Course. Online and Offline Classes

  • Do you want to be a Data scientist?
  • Do you want to be more valuable in this competitive world?
  • Do you want to increase your earning more?
  • IRA Soft Consultancy Services will provide you the best solution for your professional needs. Our systematic training program will up-skill your career to the next level.

    Get hands on experienced through our live project system and become a master on it.

    Join to us without having any prior experience in programming language and grab the data of opportunity.

    Learn a variety of tools, languages, and libraries to analyze and manipulate data, build models, and derive insights from a professional data scientist.

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About Data Science Course & Professional Certificate

Through this online Data Science and Artificial Intelligence course, you can master all basic and advanced level skills in various tools and techniques involved in the fields of Data Science, Machine Learning, Deep Learning and Artificial Intelligence. Take your career to new heights as a Data Scientist. Develop skills and practical experience that will make you job ready as a Data Scientist in 6 months. No prior experience required.

Benefits

  • Hands-on-Project
  • Certificate
  • Certified Professionals for training
  • One-on-one session with industry mentors
  • Affordable Training fee

Exclusively for

  • Engineers
  • Marketing & Sales Professionals
  • Freshers
  • Domain Experts
  • Software & IT professionals

Opportunities

  • Data Analyst
  • Data Scientist
  • Data Engineer
  • Product Analyst

Data Science Course Overview

This Data Science course using Python and R endorses the CRISP-DM Project Management methodology and contains a preliminary introduction of the same. Data Science is a 90% statistical analysis and it is only fair that the premier modules should bear an introduction to Statistical Data Business Intelligence and Data Visualization techniques. Students will grapple with Plots, Inferential Statistics, and various Probability Distributions in the module. A brief exposition on Exploratory Data Analysis/ Descriptive Analytics is huddled in between. The core modules commence with a focus on Hypothesis Testing and the "4" must know hypothesis tests. Data Mining with Supervised Learning and the use of Linear Regression and OLS to enable the same find mention in succeeding modules. The prominent use of Multiple Linear Regression to build Prediction Models is elaborated. The theory behind Lasso and Ridge Regressions, Logistic Regression, Multinomial Regression, and Advanced Regression for Count Data is discussed in the subsequent modules.

A separate module is devoted to Data Mining Unsupervised Learning where the techniques of Clustering, Dimension Reduction, and Association Rules are elaborated. The nitty-gritty of Recommendation Engines and Network Analytics are detailed in the following modules. The various Machine Learning algorithms follow next like k-NN Classifier, Decision Tree and Random Forest, Ensemble Techniques, Bagging and Boosting, Adaboost, and Extreme Gradient Boosting. Text Mining, Natural Language Processing, Naive Bayes, Perceptron, and Multilayer Perceptron are the focal points of the succeeding modules.

The fundamentals of Neural Network ANN and Deep Learning Black Box Techniques like CNN, RNN, and SVM find prominent features as well. The concluding modules contain model-driven and data-driven algorithms for Forecasting and Time Series Analysis.

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Course Curriculum

This online Data Science and Artificial Intelligence course aims to make you master in all the basic and advanced level skills in the various tools and technologies involved in the field of Data Science, Machine Learning, Deep Learning, and Artificial Intelligence.

Hours : 15
Introduction
  • What is Python?
  • Why Should I learn Python?
  • Installing Python
  • How to execute Python program
  • Write your first program
Variables & Data Types
  • Variables
  • Numbers
  • Variable Assignments
  • Introduction to Strings
  • Quick Print Check
  • Indexing and Slicing with Strings
  • String Properties and Methods
  • Print Formatting with Strings
  • Lists in Python
  • Dictionaries in Python
  • Tuples with Python
  • Sets in Python
Conditional Statements & Loops
  • Comparison Operators in Python
  • Chaining Comparison Operators in Python with Logical Operators
  • if...statement
  • if...else statement
  • elif...statement
  • The while...Loop
  • The for Loop (break, continue, pass)
  • Functions
  • Define function
  • Calling a function
  • Function arguments
  • Built-in functions
Modules & Packages
  • Modules
  • How to import a module ?
  • Command line arguments using sys module
  • Standard module- OS
  • Packages
Classes & Objects
  • Introduction about classes & objects
  • Creating a class & object
  • Inheritance
  • Methods Overriding
  • Data hiding

Hours : 25
NUMPY
  • Imports and installations
  • Array Creation Routines
  • Reading documentation within the interactive shell
  • Defining features of NumPy Arrays
  • Shape and size
  • Datatype
  • Slicing and Indexing
  • Indexing
  • Slicing
Modifying elements
  • Conditionals and Conditional Indexing
  • Array Manipulations
  • Reshape, flatten, resize and transpose
  • Broadcasting
  • Expanding and squeezing dimensions
  • Concatenating and stacking
  • Splitting
  • Append, insert and delete
  • Element-wise operations (add, multiply, etc)
  • Aggregation operations (sum, min, max, etc
  • Matrix Operations (dot products, matrix multiplication, etc)
  • Input / Output with NumPy

SCIPY
  • SCIPY IO
  • SCIPY Constants
  • SCIPY Stats
  • SCIPY SCGraph
Data Analysis
  • Introduction to Pandas (Python programming
  • language for data manipulation and analysis)
  • Understanding Series, Data Frame (rows and columns)
  • Create Data Frame and using List and Dictionary
  • Addition, Subtraction of Columns
  • Column Deletion
  • Row or column selection
Data Analysis with csv (data) file
  • How to import csv files How to take information about the different types of variables it contains How to extract information about the total rows and columns it has How to subset the data from that file
  • Removing negative values, replacing missing values with central tendencies Creating/evaluating a new column in an existing dataset
  • Renaming variable names, adding data labels
  • Filtering data according to a specific condition
  • Appending Data sets
  • Merging data sets, Removing duplicates
  • Sorting data-ascending/descending order - wrt one /two vbls
  • Creating Summaries/Reports/Tables - What a Pivot do in MS-Excel
  • Transposing a dataset
Data Visualization in Python using Matplotlib
  • Understand the matplotlib module
  • Plotting graph
  • Different main function of labeling graphs
  • Styling the graph
Introduction to Statistics
  • Supervised / Unsupervised Learning
  • Regression
  • Understand the Linear/Logical regression
  • Decision Tree

Part 1: Set up
  • Jupyter Notebook, Python Anaconda 3.6
Part 2: Python Programming
  • General Programming Structure
  • Variables and Data types
  • Nested Decision Statements
  • Use of For and While Loops
  • Use of Dictionary, Lists, Tuple, Sets
  • Use of Regular Expressions
  • Using Zip, Enumeration, *args, **kwargs
  • Using map, filter, reduce
  • Anonymous Functions, List Comprehension
  • Files - Opening, Reading, Writing, Closing
  • Regular Functions
  • Generator Functions
  • Implementing Classes and Objects
  • Multi-dimensional Lists (2D Matrix)
  • Error Handling
Part 3: ML Libraries/Modules
  • Importing Modules
  • Introduction to numPy Module
  • Introduction to sciKit Module
  • Introduction to MatplotLib Module
Part 4 : Statistical Machine Learning
  • Correlation
  • Hypothesis Testing
  • Significance Level
  • Cross Validation
  • Confidence Interval
  • Descriptive Statistics
  • Anova
  • R squared Error
Part 5: Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Gradient Descent
  • Stochiastic Gradient Descent
  • Support Vector Regression (SVR)
  • Decision Tree Regression
  • Random Forest Regression
  • Evaluating Regression Models Performance
  • Hands-on Assignments
Part 6: Classification
  • Logistic Regression
  • K-Nearest Neighbors (K-NN)
  • Support Vector Machine (SVM)
  • Kernel SVM
  • Naive Bayes
  • Decision Tree Classification
  • Random Forest Classification
  • Evaluating Classification Models Performance
  • Hands-on Assignments
Part 7: Clustering
  • K-Means Clustering
  • Hierarchical Clustering
  • Hands-on Assignments
Part 8: Association Rule Learning
  • Apriori
  • Eclat
  • Market Basket Analysis
  • FP Growth
  • Hands-on Assignments
Part 9: Reinforcement
  • Upper Confidence Bound (UCB)
  • Thompson Sampling
Part 10: Natural Language Processing (Introduction)
  • Part of Speech Tagging
  • Word sense Disambiguation
  • Natural Language Understanding
  • Natural Language Generation
Part 11: Deep Learning (Introduction)
  • Artificial Neural Networks
  • Convolutional Neural Networks
  • Recurrent Neural Network (RNN)
Part 12: Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)
  • Kernel PCA
Part 13: Model Selection & Boosting
  • Model Selection
  • Model Evaluation& Performance
  • Interview Prep : Grooming Session
  • Bonus Lecture : Review
Part 14: ML Hands-on Projects

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FAQs

Data science is a broad field that involves dealing with large volumes of data to uncover hidden trends and patterns and extract valuable information that aids in better decision-making. As companies are collecting massive amounts of data, they use various data science tools and techniques to build predictive models. IRA Soft Consultancy Services Data Science training can help you learn all of its concepts from scratch.

In a Data Science course, you need to learn about so many concepts if you are a beginner or an intermediate. A Data Science course is a training program of around six to twelve months, often taken by industry experts to help candidates build a strong foundation in the field. Apart from the theoretical material, our online Data Science certification course includes virtual labs, industry projects, interactive quizzes, and practice tests, giving you an enhanced learning experience.

A Data Scientist is an individual who gathers, cleans, analyses, and visualizes large datasets to draw meaningful conclusions and communicate them to the business leaders. The data is collected from various sources, processed into a format suitable for analysis, and fed into an analytics system where a statistical analysis is performed to gain actionable insights.

A question that I often hear from clients and colleagues is, "Why should I get a Data Science certification?" That is a fair question for most other areas of study and business. In areas such as finance or engineering, there are far more important accreditations you could and should achieve before “hanging your shingle” or trying to retool your skill set or career.

Data science is a broad discipline with a few accredited certification programs. However, many of those programs are cost-prohibitive.

although somewhat new in the nomenclature, data science encompasses many skills that professionals may already have acquired through work or educational experience such as:

Programming

  • Statistics and statistical modeling: Descriptive, diagnostic, inferential, predictive, prescriptive
  • Data visualization: Box plots, scatter plots, and more
  • Machine Learning and modeling: Regression classification, clustering, and more.

Data scientists also need to have an understanding of and exposure to reproducibility, decision-making and working with stakeholders and executives. So attaining a second (or third) degree may not be the best option for professionals looking to break into data science. Yet to be successful, they still need to communicate their experience, skills and acquired knowledge to prospective employers.

This Data Science certification training will make you learn R, Python, Machine Learning techniques, data reprocessing, regression, clustering, data analytics with SAS, data visualization with Tableau, and an overview of the Hadoop ecosystem.

Professionals who do not have any prior knowledge of the field can easily begin with this Data Science certification training as you’ll gain a thorough knowledge of the basic concepts as well.