Data Science Online Certification
Data analysis for business purposes is known as “data science.”
37 Enrolled
8 months
Course Overview
You will end up being a specialist in Data Science subsequent to finishing the webbased Data Science preparing at The Skyper. We will chip away at a few tasks and contextual investigations during the Data Science online course. Undertakings of this sort put our course aside from different courses. As you work on this venture, you will have the chance to figure out how an item is created, conveyed, and tried. You will get familiar with the prescribed procedures that can assist you with accomplishing your ideal work from now on.
Key Features

Course Duration : 8 months

RealTime Projects : 2

Project Based Learning

EMI Option Available

Certification & Job Assistance

24 x 7 Lifetime Support
 Fundamentals of Data Science and Machine Learning
 Introduction to Data Science
 Need of Data Science
 BigData and Data Science’
 Data Science and machine learning
 Data Science Life Cycle
 Data Science Platform
 Data Science Use Cases
 Skill Required for Data Science
 Mathematics For Data Science
 Linear Algebra
 Vectors
 Matrices
 Optimization
 Theory Of optimization
 Gradients Descent
 Introduction to Statistics
 Descriptive vs. Inferential Statistics
 Types of data
 Measures of central tendency and dispersion
 Hypothesis & inferences
 Hypothesis Testing
 Confidence Interval
 Central Limit Theorem
 Probability and Probability Distributions
 Probability Theory
 Conditional Probability
 Data Distribution
 Distribution Functions
 Normal Distribution
 Binomial Distribution
 An Introduction to RDBMS & SQL
 Data Retrieval with SQL
 Pattern matching with wildcards
 Basics of sorting
 Session summary
 Order by clause
 Aggregate functions
 Group by clause
 Having clause
 Nested queries
 Inner join
 Multi join
 Outer join
 Adding and Deleting columns
 Changing column name and Data Type
 Creating Table from existing Table
 Changing Constraints Foreign key
 Introduction to Stored Procedures.
 An Introduction to Python
 Why Python , its Unique Feature and where to use it?
 Python environment Setup/shell
 Installing Anaconda
 Understanding the Jupyter notebook
 Python Identifiers, Keywords
 Discussion about installed module s and packages
 Conditional Statement ,Loops and File Handling
 Python Data Types and Variable
 Condition and Loops in Python
 Decorators
 Python Modules & Packages
 Python Files and Directories manipulations
 Use various files and directory functions for OS operations
 Python Core Objects and Functions
 Built in modules (Library Functions)
 Numeric and Math’s Module
 String/List/Dictionaries/Tuple
 Complex Data structures in Python
 Python built in function
 Python user defined functions
4. Introduction to NumPy
 Array Operations
 Arrays Functions
 Array Mathematics
 Array Manipulation
 Array I/O
 Importing Files with Numpy
5. Data Manipulation with Pandas
 Data Frames
 I/O
 Selection in DFs
 Retrieving in DFs
 Applying Functions
 Reshaping the DFs – Pivot
 Combining DFs
Merge
Join  Data Alignment
6. SciPy
 Matrices Operations
 Create matrices
Inverse, Transpose, Trace, Norms , Rank etc  Matrices Decomposition
Eigen Values & vectors
SVDs
7.Visualization with Seaborn
 Seaborn Installation
 Introduction to Seaborn
 Basics of Plotting
 Plots Generation
 Visualizing the Distribution of a Dataset
 Selection color palettes
8. Visualization with Matplotlib
 Matplotlib Installation
 Matplotlib Basic Plots & it’s Containers
 Matplotlib components and properties
 Pylab & Pyplot
 Scatter plots
 2D Plots
 Histograms
 Bar Graphs
 Pie Charts
 Box Plots
 Customization
 Store Plots
9. SciKit Learn
 Basics
 Data Loading
 Train/Test Data generation
 Preprocessing
 Generate Model
 Evaluate Models
10. Descriptive Statistics
. Data understanding
 Observations, variables, and data matrices
 Types of variables
 Measures of Central Tendency
 Arithmetic Mean / Average
 Merits & Demerits of Arithmetic Mean and Mode
 Merits & Demerits of Mode and Median
 Merits & Demerits of Median Variance
11. Probability Basics
 Notation and Terminology
 Unions and Intersections
 Conditional Probability and Independence
12. Probability Distributions
 Random Variable
 Probability Distributions
 Probability Mass Function
 Parameters vs. Statistics
 Binomial Distribution
 Poisson Distribution
 Normal Distribution
 Standard Normal Distribution
 Central Limit Theorem
 Cumulative Distribution function
13. Tests of Hypothesis
 Large Sample Test
 Small Sample Test
 One Sample: Testing Population Mean
 Hypothesis in One Sample ztest
 Two Sample: Testing Population Mean
 One Sample ttest – Two Sample ttest
 Paired ttest
 Hypothesis in Paired Samples ttest
 ChiSquare test
14. Data Analysis
 Case study Netflix
 Deep analysis on Netflix data
 Exploratory Data Analysis
 Data Exploration
 Missing Value handling
 Outliers Handling
 Feature Engineering
 Feature Selection
 Importance of Feature Selection in Machine Learning
 Filter Methods
 Wrapper Methods
 Embedded Methods
 Machine Learning: Supervised Algorithms Classification
 Introduction to Machine Learning
 Logistic Regression
 Naïve Bays Algorithm
 KNearest Neighbor Algorithm
 Decision Tress
 SingleTree
 Random Forest
 Support Vector Machines
 Model Ensemble
 Model Evaluation and performance
 KFold Cross Validation
 ROC, AUC etc…
 Hyper parameter tuning
 Regression
 classification
 Machine Learning: Regression
 Simple Linear Regression
 Multiple Linear Regression
 Decision Tree and Random Forest Regression
 Machine Learning: Unsupervised Learning Algorithms
 Similarity Measures
 Cluster Analysis and Similarity Measures
 Ensemble algorithms
 Bagging
 Boosting
 Voting
 Stacking
 Kmeans Clustering
 Hierarchical Clustering
 Principal Components Analysis
 Association Rules Mining & Market Basket Analysis
7. Recommendation Systems
 collaborative filtering model
 contentbased filtering model.
 Hybrid collaborative system.
 Introduction to Git and Distributed version control
 Life Cycle
 Create clone & commit Operations
 Push & Update Operations
 Stash, Move, Rename & Delete Operations
 Artificial Intelligence
 An Introduction to Artificial Intelligence
 History of Artificial Intelligence
 Future and Market Trends in Artificial Intelligence
 Intelligent Agents – PerceiveReasonAct Loop
 Search and Symbolic Search
 Constraintbased Reasoning
 Simple Adversarial Search (GamePlaying)
 Neural Networks and Perceptions
 Understanding Feedforward Networks
 Boltzmann Machines and Autoencoders
 Exploring Backpropagation
 Deep Networks and Structured Knowledge
 Understanding Sensor Processing
 Natural Language Processing
 Studying Neural Elements
 Convolutional Networks
 Recurrent Networks
 Long ShortTerm Memory (LSTM) Networks
 Natural Language Processing
 Natural Language Processing
 Natural Language Processing in Python
 Studying Deep Learning
 Artificial Neural Networks
 ANN Intuition
 Plan of Attack
 Studying the Neuron
 The Activation Function
 Working of Neural Networks
 Exploring Gradient Descent
 Stochastic Gradient Descent
 Exploring Backpropagation
 Artificial and Conventional Neural Network
 Understanding Artificial Neural Network
 Building an ANN
 Building Problem Description
 Evaluation the ANN
 Improving the ANN
 Tuning the ANN
 Image Processing / Machine Vision
 Image basics
 Loading and saving images
 Thresholding
 Bluring
 Masking
 Image Augmentation
 Conventional Neural Networks
 CNN Intuition
 Convolution Operation
 ReLU Layer
 Pooling and Flattening
 Full Connection
 Softmax and CrossEntropy
 Building a CNN
 Evaluating the CNN
 Improving the CNN
 Tuning the CNN
 Recurrent Neural Network
 Recurrent Neural Network
 RNN Intuition
 The Vanishing Gradient Problem
 LSTMs and LSTM Variations
 Practical Intuition
 Building an RNN
 Evaluating the RNN
 Improving the RNN
 Tuning the RNN
 Time Series Data
 Introduction to Time series data
 Data cleaning in time series
 PreProcessing Time series Data
 Predictions in Time Series using ARIMA, Facebook Prophet models.
 Artificial Intelligence
Machine Learning Features and Services
 Using python in Cloud
 How to access Machine Learning Services
 Lab on accessing Machine learning services
 Uploading Data
 Preparation of Data
 Applying Machine Learning Model
 Deployment by publishing Models using AWS or other cloud computing
1. Introduction to Data Visualization and the Power of Tableau
 Architecture of Tableau
 Product Components
 Working with Metadata and Data Blending
 Data Connectors
 Data Model
 File Types
 Dimensions & Measures
 Data Source Filters
 Creation of Sets.
2. Scatter Plot
 Gantt Chart
 Funnel Chart
 Waterfall Chart
 Working with Filters
 Organizing Data and Visual Analytics
 Working with Mapping
 Working with Calculations and Expressions
 Working with Parameters
 Charts and Graphs
 Dashboards and Stories
 Machine Learning end to end Project blueprint
 Case study on real data after each model.
 Regression predictive modeling – Ecommerce
 Classification predictive modeling – Binary Classification
 Case study on Binary Classification – Bank Marketing
 Case study on Sales Forecasting and market analysis
 Widespread coverage for each Topic
 Various Approaches to Solve Data Science Problem
 Pros and Cons of Various Algorithms and approaches
 AmazonRecommender
 Image Classification
 Sentiment Analysis
Project Domains: Finance
 Insurance company wants to decide on the premium using various parameters of the client.
 It’s an important problem to keep the clients and attract new ones.
By completing this project you will learn:
 How to collect data?, how to justify right features? , Which ML / DL model is best in this situation? How much data is enough?
 How to have CI/CD in the project?
 How to do Deployment of Project to cloud?
Image Processing in Health care
 A hospital wants to automate Detection of pneumonia in Xrays using image processing.
By doing this project you will understand
 How to handle image data? How to preprocess and augment image data? How to choose right model for image process?
 How to apply transfer learning in image processing?
 How to do incremental learning & CI/CD in the project?
 How to do Deployment of Project to cloud?
Natural Language Processing
 One of the companies wants to automate applicant’s level in English communication.
 Create a ML/DL model for this task.
By completing this project you will learn
 How to do convert text to right representation? How to preprocess text data? How to select right ML/DL model for text data ?
 How to do transfer learning in Text Analytics?
 How to do CI/CD in text analytics project?
 How to do Deployment of Project to cloud?
Mechanical
 A mechanical company wants to perform predictive maintenance of engine parts.
 This enables company to efficiently change parts before machine fails.
By performing this task you will learn,
 How to handle time series data?
 How to preprocess time series data?
 How to create ML/DL model for Time series Data?
 How to do CI/CD in text analytics project?
 How to do Deployment of Project to cloud?
Sales / Demand Forecasting
 Predict the sales / demand of a product of a company.
 Sales / Demand forecasting of the product will help company efficiently manage the resources.
 Create a ML/DL model for this problem.
By performing this project you will learn,
 How to handle time series data?
 How to preprocess time series data?
 How to create ML/DL model for Time series Data?
 How to do CI/CD in text analytics project?
 How to do Deployment of Project to cloud?
Frequently ask question
Everything relies upon the clusters you like to go with. This fullstack engineer online course will take around 2 to 2 and a half months to probably finish
To apply for this fullstack web based preparing, most essentially have the important information about what information structure, programming language, OOPs, and web basics are.
We furnishes you with the best internet based concentrate on materials and meetings, which will assist you with understanding things quicker. You will likewise get active every one of the constant tasks and situations that happened in the current business. With this, our educators are so all around encountered that they will assist you with every one of the issues and issues you face during your training in principle and practices function also.
We likewise give 100 percent arrangement help after the full stack online course is finished.
We furnishes you with the best internet based concentrate on materials and meetings, which will assist you with understanding things quicker. You will likewise get active every one of the constant tasks and situations that happened in the current business. With this, our educators are so all around encountered that they will assist you with every one of the issues and issues you face during your training in principle and practices function also.
We likewise give 100 percent arrangement help after the full stack online course is finished.
A fullstack web designer requirements to have magnificent information about both frontend and backend improvement. Also, subsequently, this profile goes more sought after with more significant compensation bundles and climbing. So to go with this full stack engineer web based preparing with Python, this is the right stage for you to improve your advancement abilities, and picking this vocation way will add extraordinary varieties to your future expert life.
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8 months
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2
25
English
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3.5 months