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Course Section:
  • Summary
  • Highlights
  • Syllabus

In the data-driven world, Artificial Intelligence and Machine Learning are becoming the most in-demand career option. Artificial Intelligence and Machine Learning are the studies of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of\ndata science is to gain insights and knowledge from any type of data — both structured and\nunstructured. It involves the use of tools/technologies for managing large amounts of data, statistical techniques to analyze and interpret this data, and an understanding of domains such as finance, marketing, and pharma which provides the context for such analysis. In the data-driven world, Artificial Intelligence and Machine Learning are becoming the most in-demand career option. Artificial Intelligence and Machine Learning are the studies of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured. It involves the use of tools/technologies for managing large amounts of data, statistical techniques to analyze and interpret this data and an understanding of domains such as finance, marketing, and pharma which provides the context for such analysis.

  • Microsoft Excel
  • Statistics
  • R Language
  • Python
  • Machine Learning
  • Advance AI & ML 1
  • Advance AI & ML 2
  • Data Visulation
  • SQL ( Database Management System )
  • Big Data
  • Domain Analytics
  • Live Project ( Any 2)
6 Hours 4 Lecture
Lecture 1 : Basic of Excel
Lecture 2 : Data Analysis in Excel
Lecture 3 : Formula, Loops
Lecture 4 : Google Sheets
15 Hours 18 Lecture
Lecture 1 : What is Statistics
Lecture 2 : Types of Statistics
Lecture 3 : Graph
Lecture 4 : Charts
Lecture 5 : Charts
Lecture 6 : Distribution
Lecture 7 : Standard
Lecture 8 : Deviation
Lecture 9 : Variance
Lecture 10 : Measure of Dispersion
Lecture 11 : Sampling Probability Theorems
Lecture 12 : Type of Probability
Lecture 13 : Distribution
Lecture 14 : Z score
Lecture 15 : Hypotheses Testing
Lecture 16 : Central Limit Thermos
Lecture 17 : Types of Hypothesis Testing
Lecture 18 : Types of errors in Hypotheses Testing
10 Hours 12 Lecture
Lecture 1 : Download and installation
Lecture 2 : Introduction to R
Lecture 3 : Basic to R
Lecture 4 : Wring a code
Lecture 5 : Operators
Lecture 6 : Function
Lecture 7 : Data Structure
Lecture 8 : Data type
Lecture 9 : Visualization
Lecture 10 : Packages
Lecture 11 : Conditional statements
Lecture 12 : Loops
10 Hours 14 Lecture
Lecture 1 : Download and installation
Lecture 2 : Introduction to Python
Lecture 3 : Basic to Python
Lecture 4 : Wring a code
Lecture 5 : Operators
Lecture 6 : Function
Lecture 7 : Data Structure
Lecture 8 : Data type
Lecture 9 : Visualization
Lecture 10 : Libraries
Lecture 11 : Conditional statements
Lecture 12 : loops
Lecture 13 : File Handling
Lecture 14 : LOOPS
40 Hours 7 Lecture
Lecture 1 : Linear Regarrison
Lecture 2 : Logistics Regarrison
Lecture 3 : Clustering
Lecture 4 : Decision tree
Lecture 5 : Random Forest
Lecture 6 : Time Series
Lecture 7 : Support Vector Machine
50 Hours 6 Lecture
Lecture 1 : Ensemble Modelling
Lecture 2 : PCA, LDA
Lecture 3 : Bagging and boosting techniques
Lecture 4 : Neural Network
Lecture 5 : Deep Learning
Lecture 6 : Tensor Flow
60 Hours 4 Lecture
Lecture 1 : Recommendation Engine (Recommender system)
Lecture 2 : Advance Text Analytics and NLP
Lecture 3 : Introduction to Chat Bot’s
Lecture 4 : Introduction to Computer Vision
15 Hours 3 Lecture
Lecture 1 : Tableau
Lecture 2 : Power BI
Lecture 3 : Case Study
8 Hours 6 Lecture
Lecture 1 : Install My SQL Work Bench
Lecture 2 : Introduction to SQL
Lecture 3 : Basic command in SQL
Lecture 4 : Advance SQL Coding
Lecture 5 : SQL data base overview
Lecture 6 : SQL with Python
10 Hours 3 Lecture
Lecture 1 : Introduction to Big Data Analytics
Lecture 2 : Hadoop
Lecture 3 : Spark
32 Hours 7 Lecture
Lecture 1 : Domain Analytics
Lecture 2 : Marketing Analytics
Lecture 3 : Retails Analytics
Lecture 4 : Finance Analytics
Lecture 5 : HR Analytics
Lecture 6 : Health Care Analytics
Lecture 7 : Sports Analytics
Hours 8 Lecture
Lecture 1 : Recommendation Engine
Lecture 2 : Chat Bot
Lecture 3 : Marketing Analytics
Lecture 4 : Retails Analytics
Lecture 5 : Finance Analytics
Lecture 6 : HR Analytics
Lecture 7 : Health Care Analytics
Lecture 8 : Sports Analytics
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Frequently Asked Questions

To enroll in the course, you must have completed your 10+2 or pursued graduation from any recognised university.

Data Science is forecasted to become a 77 billion dollar industry by 2023. Honing in-demand skills such as this can help in landing a lucrative package.

  • Must complete their graduation in any stream.
  • ICA shall only be assisting the candidates in finding a paid internship in case of students pursuing graduation or are undergraduate after completion of course.
  • ICA has placement offices across 30+ cities with a huge employer base. Our priority will be to place the candidate within the city nearest to their place. In case of vacancies not arising during the specific time, we shall be providing the opportunities across other cities.
  • Students must clear all the assessments post completion of every such module followed by one common/final exam towards the end.
  • If a student is found using malpractice/unfair means, he will be suspended.
  • In case of Student’s knowledge is not found sufficient, ICA may call the student to appear for assessment in person at the center.

The individual must: 

  • Have completed their graduation from any domain.
  • ICA has placements in more than 30 cities spanning across India. We will try to place the candidates nearest to their hometown. However, if there are no vacancies during that certain period, we shall provide placement opportunities in other cities.
  • Students must be able to clear all the assessments post completion of every such module with an examination at the end.

  • For Classroom training nothing is required as our classrooms are well equipped with high end computers & stable internet connection
  • Smartphones are required to access the e-content.

  • For Classroom training examinations will be conducted in the center only.
  • Common / Final exam will be conducted after completion of all the modules and their assessments.
  • Student identity validation and surveillance is a must during the exam time.
  • Non-submission of assessments in time will be considered as if not attempted.

  • Must complete their graduation in any stream.
  • ICA shall only be assisting the candidates in finding a paid internship in case of students pursuing graduation or are undergraduate after completion of course.
  • ICA has placement offices across 30+ cities with a huge employer base. Our priority will be to place the candidate within the city nearest to their place. In case of vacancies not arising during the specific time, we shall be providing the opportunities across other cities.
  • Students must clear all the assessments post completion of every such module followed by one common/final exam towards the end.
  • If a student is found using malpractice/unfair means, he will be suspended.
  • In case of Student’s knowledge is not found sufficient, ICA may call the student to appear for assessment in person at the center.

Upon completion of your course, clearing your assessments and projects, you will receive the certificate by ICA Edu Skills.

  • Students will have access to ICA’s Learnersmall Application on Google Play Store.
  • E-content has been made available in the English language along with various notes, self-assessments and mock examinations.
  • Physical books are also available as an option but at an extra cost.

No, you will be getting a soft copy of the certificate in your student portal.

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