Professional Certificate in Data Science & Machine Learning

  • 40 weeks
  • 240 Hours

A successful career is just one step away.

Course Overview

This Data Science & Machine Learning program includes both case study as well as capstone project. We cover critical topics on Data Science, Machine Learning algorithms, along with practicalprojects which helps in better understanding. This course deals with preparing data for analysis and processing, performing advanced data analysis, and presenting the resultsto reveal patterns and enable stakeholders to draw informed conclusions.

Advantage

Professional Certificate in Data Science & Machine Learning is best data course led by the experienced faculty members aims at helping you to get ready for industry from basic and advanced level skills which are crucial inthe field of Data Science, Machine Learning, Deep Learning, and Artificial Intelligence. This blended program covers all the leading software like R, Python, Power BI, SQL, and Excel.

What you'll learn

  • Python Programming
  • Statistics
  • Machine Learning
  • SQL
  • R Programming
  • Advanced Excel
  • Power BI

Data Science Using Python

  • What is Data Science?
  • A comparative study between Data Science and Big Data Analytics.
  • Types of Data.
  • The Data Science Lifecycle
  • Data Acquisition and Preparation
  • Data Modelling and Visualization
  • Data Science Roles
  • Benefits of Data Science
  • Challenges of Data Science
  • Business Use Cases for Data Science
  • Concept of Analytics and Statistics
  • Categories of Analytics
  • Properties of Measurement
  • Scales of Measurement
  • Concept of Data visualization
  • Measures of Central Tendency
  • Measures of Dispersion
  • Moments, Skewness and Kurtosis
  • Concept of Correlation and Covariance
  • Introduction to Probability Theory
  • Probability Distributions
  • Sampling and Estimation
  • Testing of Hypothesis
  • Introduction to python
  • History of Python
  • Internal & External IDLE
  • Installation of Python & Anaconda
  • Compiler & Interpreter
  • Write your first program
  • Data types, Input and output function
  • Types of Operators
  • Conditional Statement: if-else, if-else-if, Nested if else
  • Loop: While loop, For loop
  • Nested while loop, Nested for loop Break, Continue and Pass
  • Basic Data Types- Numeric & String
  • Tuple and it’s operation
  • List and it’s operation
  • Dictionary and it’s operation
  • Sets and it’s operation
  • Basics Defining function
  • Function call Return statement
  • Function with parameter and without parameter
  • Local and global variable
  • Recursion, Anonymous (lambda) function
  • User defined functions
  • OOPS concepts Defining
  • Class Creating object, Constructor
  • Method vs function Calling methods
  • Method Overriding, List of objects Inheritance
  • Defining a file, Types of files and its operations
  • Python read Files
  • Python Write/Create Files
  • Python Delete Files
  • Pickle Module
  • Introduction to NumPy, Pandas, Matplotlib
  • Array, Array indexing, Array operation
  • Data frame, series, Group by
  • Missing values
  • Box plot, Scatter plot, Chart styling
  • Histogram, Bar chart etc.
  • Group by plotting
  • Concept of Supervised learning
  • Concept of Unsupervised learning
  • Concept of Reinforcement learning
  • Simple Linear Regression
  • Multiple Linear Regression
  • Implementation of Linear Regression
  • Advanced Topics: Normal Equation, Polynomial Regression, R-sq. Score
  • Python Implementation
  • Concept and Theory
  • Sigmoid function
  • Mathematical Concepts of Logistic Regression
  • Binary and Multivariate Classification Problems
  • Implementation of Logistic Regression
  • K-Nearest Neighbors-Concept and Theory
  • Implementation of K-Nearest Neighbors
  • Support Vector Machine (SVM)-Concept and Theory
  • Implementation of Support Vector Machine
  • Naïve Bayes Classifier- Concept
  • Implementation of Naïve Bayes Classifier
  • Decision Tree Classifier-Concept
  • Implementation of Decision Tree Classifier
  • Random Forest Classifier-Concept
  • Implementation of Random Forest Classifier
  • Dimensionality Reduction Problem- Curse of Dimensionality
  • Principal Component Analysis (PCA)
  • Implementation of PCA
  • K-Means Clustering- Concept
  • Implementation of K-Means Clustering
  • Hierarchical Clustering- Concept
  • Implementation of Hierarchical Clustering
  • DBSCAN Clustering-Concept
  • Implementation of DBSCAN Clustering
  • Introduction of Deep Learning and Neural Network
  • Types and Applications of Neural Network
  • Skills required for Neural network
  • Why Python is best for Neural Network
  • Anaconda Installation: Spyder & Jupiter Notebook
  • Introduction to Keras & Tensor Flow
  • Installation of Keras & Tensor Flow
  • ANN and Neuron Structure
  • How does Neural Network Works?
  • Practical Implementation of ANN
  • Train-Test Splitting
  • ANN model Training
  • Activation Function
  • Fit all the Layers
  • Backpropagation
  • Fitting to the training Dataset and finding Accuracy
  • Image Reading and CNN Process
  • Steps of CNN
  • Conclusion of CNN Process
  • Importing Required libraries
  • Reading Cat & Dog Dataset
  • Applying CNN layers
  • Fitting the Dataset in Model
  • Visualization of Accuracy and Loss
  • Prediction with single image
  • Introduction and Application
  • Process of RNN, Types of RNN, Gradient Problem
  • LSTM & GRU Explanation
  • Steps of LSTM
  • Creation of Data Structure with Time Steps
  • LSTM layers
  • Google Stock market prediction

Data Science Using R

  • History of R-language
  • Why to learn R-language ?
  • Importance of R-language
  • Installation and setup Environment
  • Packages interfaces and library
  • Expressions and Operations
  • Data Types and Data Structures- Vectors, Factors, Matrix, Data frames, Lists
  • Vector Basics
  • Vector Operations
  • Vector Indexing and Slicing
  • Matrix Operations Data Science using R
  • Data Frame Indexing and Selection
  • Operations on Data Frame
  • CSV Files with R
  • Operators
  • Conditional Statements
  • Loops & Functions
  • Built-in R Features & Apply
  • Dates and Timestamps
  • Understanding & Working with Graph Libraries.
  • Overview of ggplot2
  • Histograms
  • Scatterplots
  • Bar Plot
  • Box plots
  • 2 Variable Plotting
  • Sorting, Concatenation of Datasets
  • Concept of Hypothesis.
  • Null Hypothesis
  • Alternative Hypothesis
  • Type-I error
  • Type-II error
  • Level of Significance
  • Confidence Intervals
  • Parametric Tests and Non-Parametric Tests
  • One Sample T test
  • Two Independent Sample T test
  • Paired Sample T test
  • Chi-square Test for Independence of Attributes
  • Principal Component Analysis
  • Concept of Communalities
  • Eigen Values and Eigen Vectors
  • Correlation Matrix check and KMO-MSA check
  • Factor loading Matrix
  • Diagrammatic Representation of Factors
  • Problems of Factor Loadings and Solutions
  • Introduction to Machine Learning
  • Data Munging in R
  • Cyclical vs Seasonal Analysis
  • One Way Anova
  • Two Way Anova
  • Concept of Linear Regression.
  • Important features of a Straight line.
  • Method of least Square.
  • Assumptions of Classical Linear Regression Model
  • Understanding the Goodness of Fit
  • Test of Significance of the Estimated parameters.
  • Concept of multicollinearity
  • Concept of VIF
  • Concept of AutoCorrelation
  • Practical Application of Linear Regression using R.
  • Concept of Logistic Regression.
  • Differences between Linear Regression and Logistic Regression.
  • Logistic Regression Model.
  • ODDS AND ODDS RATIO-Mathematical Concepts
  • Concept of Concordant Pairs, Discordant Pairs, Tied Pairs.
  • Confusion Matrix and its Measures
  • Determining the Cut-Point Probability Level.
  • Receiver Operating Characteristic Curves
  • Practical Application of Logistic Regression using R.
  • Concept of Time Series and its Applications
  • Assumptions of Time Series Analysis
  • Components of Time Series
  • Smoothening techniques
  • Stationarity
  • Random Walk
  • ARIMA Forecasting
  • Box Jenkins Technology
  • Merits and Demerits of BJ Technology
  • Concept of Decision Tree
  • Decision Tree Application using R.
  • Concept of K-Means Clustering.
  • Types of Cluster Analysis.
  • Concept of Linkage.
  • Ward’s Minimum Variance Criteria.
  • Clustering related Statistics-Semi-Partial R-Square, R Square
  • Graphical Representation of Cluster Analysis
  • Practical Application of Clustering using R.
  • Concept of Text Mining and Sentiment Analysis
  • Concept of Stopwords
  • Practical Application of Text Mining and Sentiment Analysis
  • Concept of Market Basket Analysis
  • Measures of Market Basket Analysis-Support, lift, Confidence
  • Advantages of Market Basket Analysis
  • Practical Application of Market Basket Analysis.

SQL

Power BI (Self-paced)

  • Introduction to Microsoft Power BI - Learning Outcomes
  • Introduction to Power BI
  • Getting Started with Power BI Pro
  • Working with Various Data Sources
  • Improve Power BI Reports
  • Optimize Power BI Reports
  • Enriching Visualization
  • Introduction to Microsoft Power BI - Lesson Summary
  • Working with Power BI - Learning Outcomes
  • Working with Data - Advanced Techniques
  • Power BI Service - The Online Version of Power BI
  • Publish Reports
  • The Various Power BI Components and How to Install Power BI
  • Working with Power BI - Lesson Summary

Advanced Excel (Self-paced)

  • Introduction to MS Excel
  • Difference between Excel 2003, 2007, 2010 and 2013
  • Saving Excel File as PDF, CSV and Older versions
  • Using Excel Shortcuts with Full List of Shortcuts
  • Copy, Cut, Paste, Hide, Unhide, and Link the Data in Rows, Columns and Sheet
  • Using Paste Special Options
  • Formatting Cells, Rows, Columns and Sheets
  • Protecting & Un protecting Cells, Rows, Columns and Sheets with or without Password
  • Page Layout and Printer Properties
  • Inserting Pictures and other objects
  • Lookup and Reference Functions
  • Logical Function
  • Database Functions
  • Information Functions
  • Math and Trigonometry Functions
  • Statistical Functions
  • Text Functions
  • Using Conditional Formatting
  • Using Conditional Formatting with Multiple Cell Rules
  • Using Colour Scales and Icon Sets in Conditional Formatting
  • Creating New Rules and Managing Existing Rules
  • Sorting Data, A-Z and Z-A
  • Using Filters to Sort Data
  • Advance Filtering Options Pivot Tables
  • Creating Pivot Tables
  • Using Pivot Table Options
  • Changing and Updating Data Range
  • Formatting Pivot Table and Making Dynamic Pivot Tables
  • Creating Pivot Charts
  • Types of Pivot Charts and Their Usage
  • Formatting Pivot Charts and Making Dynamic Pivot Charts

Admission Process

Please call to admission counselor for course fees, registration fees, EMI fecilities,registration form and other formalities. Contact to admission counselor
+91-9831765780
+91-9830247087

Who can join?

Any graduate with knowledge of besic computing.

Requirment

1.Personal computer/laptop with webcam and microphone
2.Stable internet connections

Payment details

Bank Details:
KLMS HANDS-ON SYSTEMS PRIVET LIMITED
Account Number: 19700200000420
IFSC Code: BARB0SALTLA (5th letter is numeric zero)
UPI Payment: 9432257052@okbizaxis

data-science-&-machine-learning.png
This Course Include:
  • Live Instructor-Led Course
  • Project and Case Studies
  • Certificate of completion
  • Learn from Experts
  • Placement Assistance
  • Assistance for Global Certification
Download Curriculam Enroll Now