What you'll learn

  • Python core
  • Modules exception handling
  • Rest API
  • Database
  • Exploratory Data Analysis
  • Statistics
  • Machine Learning
  • Data Science In various domain
  • Project Architecture For Managers
  • Risk Involvement in AI Solution

Why Learn Data Science Managers Programs At iNeuron?

Data science for managers at iNeuron will provide managers with the right set of skills and management techniques giving them a competitive advantage over others to effectively manage the workforce, create innovative solutions and drive the organisation to its ultimate goal efficiently.

  • Students:1400+
  • Duration:9 months
  • Major Projects:20
  • Minor Projects:10
  • Mode:Online
  • Job Assistence:100%
  • Certificate:Yes
  • Mock Interviews:Yes

Course Curriculum

  • Introduction of python and comparison with other programming languages
  • Installation of Anaconda Distribution and other python IDE
  • Python Objects, Number & Booleans, Strings, Container objects, Mutability of objects
  • Operators – Arithmetic, Bitwise, comparison and Assignment operators, Operators Precedence and associativity.
  • Conditions(If else,if-elif-else)
  • Loops(While ,for)
  • Break and Continue statements
  • Range functions

  • String object basics
  • String methods
  • Splitting and Joining Strings
  • String format functions
  • List object basics
  • List methods
  • List as stack and Queues
  • List comprehensions

  • Tuples, Sets, Dictionary Object basics, Dictionary
  • Object methods,Dictionary View Objects. Functions basics, Parameter passing, Iterators
  • Generator functions
  • Lambda functions
  • Map, Reduce, filter functions

  • Creating classes and Objects
  • Inheritance,Multiple Inheritance
  • Working with files
  • Reading and writing files
  • Buffered read and write
  • Other File methods

  • Creating new modules
  • Exceptions Handling with Try-except
  • Creating, inserting and retrieving Table
  • Updating and deleting the data.

  • Matplotlib
  • Seaborn
  • Plotly
  • Cufflinks

  • Flask introduction
  • Flask Application
  • Open link Flask
  • App Route Flask
  • URL Building Flask
  • HTTP Methods Flask
  • Templates Flask
  • Django end to end

  • Mongo DB
  • SQL lite
  • Python SQL

  • Web crawlers for image data sentiment analysis and product review sentiment analysis
  • Integration with web portal
  • Integration with rest api ,web portal and mongodb on Azure
  • Deployment on web portal on Azure
  • Text mining
  • Social media data churn

  • Python Pandas – Series
  • Python Pandas – DataFrame
  • Python Pandas – Panel
  • Python Pandas – Basic Functionality
  • Descriptive Statistics
  • Function Application
  • Python Pandas – Reindexing
  • Python Pandas – Iteration
  • Python Pandas – Sorting
  • Working with Text Data
  • Options & Customization
  • Indexing & Selecting Data
  • Statistical Functions
  • Python Pandas – Window Functions
  • Python Pandas – Date Functionality
  • Python Pandas – Timedelta
  • Python Pandas – Categorical Data
  • Python Pandas – Visualization
  • Python Pandas – IO Tools

  • NumPy – Ndarray Object
  • NumPy – Data Types
  • NumPy – Array Attributes
  • NumPy – Array Creation Routines
  • NumPy – Array from Existing Data
  • Array From Numerical Ranges
  • NumPy – Indexing & Slicing
  • NumPy – Advanced Indexing
  • NumPy – Broadcasting
  • NumPy – Iterating Over Array
  • NumPy – Array Manipulation
  • NumPy – Binary Operators
  • NumPy – String Functions
  • NumPy – Mathematical Functions
  • NumPy – Arithmetic Operations
  • NumPy – Statistical Functions
  • Sort, Search & Counting Functions
  • NumPy – Byte Swapping
  • NumPy – Copies & Views
  • NumPy – Matrix Library
  • NumPy – Linear Algebra

  • Feature_Engineering_and_Selection
  • Building_Tuning_and_Deploying_Models
  • Analyzing_Bike_Sharing_Trends
  • Analyzing_Movie_Reviews_Sentiment
  • Customer_Segmentation_and_Effective_Cross_Selling
  • Analyzing_Wine_Types_and_Quality
  • Analyzing_Music_Trends_and_Recommendations
  • Forecasting_Stock_and_Commodity_Prices

  • Descriptive Statistics
  • Sample vs Population statistics
  • Random Variables
  • Probability distribution function
  • Expected value
  • Binomial Distribution
  • Normal Distributions
  • Z-score
  • Central limit Theorem
  • Hypothesis testing
  • Z-Stats vs T-stats
  • Type 1 type 2 error
  • Confidence interval
  • Chi-Square test
  • ANOVA test
  • F-stats

  • What is Data Science?
  • Why Data Science is Important?
  • Data Science in Business?
  • Why Data Science Importance For Manager?
  • What Skill Set For Data Science Manager?
  • Steps to find out your Data Science Team?
  • Different ways to manage your Data Science Team?
  • Success & Failure for your Data Science Team?
  • Embedded Teams vs. Dedicated Groups
  • Internal problems while leading with your Data Science Team?
  • Application of Data Science?
  • Understanding Customer Segmentation Workflow?
  • Building a Customer Service ChatBoT for your Business?
  • Understand End to End workflow of ChatBot for Business?

  • Reading CSV, JSON, XML, .XLSX and HTML files using python ETL operations in python
  • Sorting/ merging data in python
  • Data Cleaning with python
  • Important Steps to keep in mind for Data Wrangling

  • Introduction
  • Supervised, Unsupervised, Semi-supervised, Reinforcement Train, Test, Validation Split
  • Performance
  • Overfitting ,underfitting

  • What is Linear Regression?Advantage & disadvantage?
  • Assumptions
  • R square adjusted R square
  • Intro to Scikit learn
  • Training methodology
  • Hands on linear regression
  • Ridge Regression
  • Logistics regression
  • Precision Recall
  • ROC curve
  • F-Score
  • Case study based on Regression Model
  • Deployment from scratch to advance with flask

  • What is Decision Tree?Advantage & Disadvantage?
  • Cross Validation
  • Bias vs Variance
  • Ensemble approach
  • Bagging Boosting
  • Randon Forest
  • Variable Importance

  • XGBoost
  • Hands on XgBoost
  • H Nearest Neighbour
  • Lazy learners
  • Curse of Dimensionality
  • HNN Issues
  • Hierarchical clustering
  • H-Means
  • Performance measurement
  • Principal Component analysis
  • Dimensionality reduction
  • Factor Analysis

  • SVR
  • SVM
  • Polynomial
  • Regression
  • Ada boost
  • Gradient boost
  • Gaussian mixture
  • Anamoly detection
  • Novelty detection algorithm
  • Stacking
  • K-nn regressor
  • Decision tree regressor
  • DBSCAN

  • How Data Science is helping HealthCare?

    Real-Time Use Cases

    Project InnerEye

    Patient Risk Identification

    Visual Data Processing for Tumor Detection

  • Analyzing, Visualizing of all use cases & workflow from scratch to advance.
  • Real time project and deployment

  • How can we use Data Science in Warehouse?

    Real-Time Use Cases

    Track and Warehouse Analysis

    Logistics Route Optimization

    Workforce Planning

  • Analyzing and Visualizing of all use cases & workflow from
  • Scratch Real time project and deployment

  • How can retailers benefited themselves with Data Science?

    Real-Time Use Cases

    Recommendation engine

    Price Optimization

    Location of new stores

  • Analyzing and Visualizing of all use cases & workflow from scratch
  • Real time project and deployment

  • How Data Science is Helping Energy & Utility?

    Real-Time Use Cases

    Failure Probability Modelling

    Demand Response Management

    Real Time Customer Billing

  • Analyzing and Visualizing of all use cases & workflow from scratch
  • Real time project and deployment

  • How Data Science is Helping Finance?

    Real-Time Use Cases

    Automating Risk Management

    Real-Time Analytics

    Deep Personalization & Customization

  • Analyzing and Visualizing of all use cases & workflow from scratch
  • Real time project and deployment

  • How Data Science is Helping in Education?

    Real-Time Use Cases

    Monitoring Students Requirements

    Measuring Instructor Performance

    Innovating The Curriculum

  • Analyzing and Visualizing of all use cases & workflow from scratch
  • Real time project and deployment

  • Different platform for Deployment(understanding from scratch to advance):

    CloudFoundary

    AWS

    AZURE

    Google Cloud Platform

    Expose API to Web Browser & Mobile application

    DevOps infrastructure For Machine Learning Model

    DataBase integration & scheduling of Machine Learning

    Custom Machine Learning Training Approach

    Discussion on Infra Cost & Data Volume

    Prediction Based on Streaming Data

  • Understanding Project Architecture Pipeline?
  • How to Define Best Fit Pipeline For Your Project?
  • Defining Advantage & Disadvantage For Your Project Pipeline?
  • Understanding SCRUM and Agile Process?
  • Full Architecture Workflow From Scratch To Advance?
  • Real Time Project Implementation!!

  • What is AI & Why it is mostly used?
  • Identify the Problem Statement You Want to Solve with AI?
  • Prioritize Dimension of Potential & Feasibility?
  • Acknowledge internal Gap with your AI Solution?
  • Choose your Team & Set up with your Pilot Project?
  • Form a Strong Team to Integrate Data?
  • Get Ready with your Pilot Project to Test?
  • Find out the Glitch that you have to work with?
  • Figure out your Expenses & Work Accordingly?

  • Different Ways to Analyze whether Your AI Solution is Secure or NOT?
  • Understanding Security Barriers For AI Solution?
  • Redesign the Workflow & Pipeline?
  • Find new Glitch & Apply Accordingly?
  • Understanding Cost-Cutting For Your AI Solution?
  • Workflow from Scratch to Advance!!

End to end Scenario based interview preparation for every individual

  • One to one resume Discussion with project, technology and Experience
  • Mock interview for every students’ multiple rounds

  • At least three referrals for every student in MNC startups and product-based company

Projects

  • Autonomous Tagging of Stack Overflow Questions.
  • Keyword/Concept Identification.
  • Topics Identification.
  • Automated Essay Grading.
  • Sentence to Sentence Semantic Similarity.
  • Fight online abuse.
  • Open Domain Question Answering.
  • Automatic Text Summarization.
  • Copy-cat Bot.
  • Sentiment Analysis.
  • De-anonymization.
  • Univariate Time Series Forecasting.
  • Multivariate Time Series Forecasting.
  • Demand/load forecasting.
  • Predict Blood Donation.
  • Movie Recommender.
  • Search + Recommendation System.
  • Image Classification.
  • Bone X Ray Competition.
  • Image Captioning.
  • Image Segmentation/Object Detection.
  • Video Summarization.
  • Style Transfer.
  • Face Recognition.
  • Clinical Diagnostics: Image Identification classification & segmentation.
  • Satellite Imagery Processing for Socioeconomic Analysis.
  • Satellite Imagery Processing for Automated Tagging.
  • Music/Audio Recommendation Systems.
  • Music Genre recognition using neural networks.

  • Statistical project.
  • Traffic surveillance system.
  • Object identification.
  • Object tracking.
  • Object classification.
  • Tensorflow object detection.
  • Image to text processing.
  • Speech to speech analysis.

  • Vision based Attendance system.
  • Vision based sentiment analysis.
  • Raspberry pi integration.
  • Azure cloud Integration.
  • Deployment in ML devOps pipeline.
  • Autonomous vehicle.
  • Custom object training using TFOD.
  • Google coral deployment of Computer vision project.
  • Nvidia jetson nano deployment of computer vision project.
  • End to end cloud.
  • Deployment of computer vision, machine learning and NLP project.
  • Surveillance system for Warehouse.
  • Truck licence plate detection and its integration with IP camera.
  • Drone image analysis using DJI tello.

Batch Timings & Fee

Fee Structure

₹3,540

incl GST
Online

Start Date : 28th Mar 2020

Sat & Sun: 10.00 AM - 12.00 PM

Thu(Doubt Clearing): 10.00 PM - 12.00 AM

Data Science for Managers from iNeuron

Complete your Data Science for Managers program at iNeuron along with your internship and get your certificate.

  • 6 Months live session training
  • Placement Assistance
  • 3 Months in-house internship
Certificate

Our Features

6 Months Classroom Training

Once on-board our candidates will go through an intense classroom training session of 3 months conducted by our best team of experienced senior data scientists, who will provide all conceptual knowledge in an innovative as well as an interactive manner.

Career Counselling

After successful completion of our course, we train our candidates via mock interviews, personal interviews, and group discussions as well as provide them with professional mentoring and help them build an attractive resume which will enable them to fetch a lucrative job.

3 Months Of In-House Projects

What makes us different from other training institutes is that we are also into product development and that enables us to provide hands-on experience to our candidates to contribute in live projects, which will result in a deeper understanding of the course with industry-level knowledge.

Admission Process

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