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AIDM : ISO APPROVED 9001 : 2015

CERTIFICATE NO. 0606Q80220

best java training institute in delhi

AIDM : ISO APPROVED 9001 : 2015

CERTIFICATE NO. 0606Q80220




 Data science training


5 Star Rating: Very Good 4.50 out of 5 based on 431 ratings.

AIDM is well-recognized data science training institute in Laxmi Nagar, which includes practical training sessions in the computer labs with live project and simulations. As we are known for the best institute which offers the best data science course in Delhi with detailed modules on every topic starting from the basics of data science to the advanced level. Our trainers are experts in this industry with an excellent capability of delivering useful knowledge about the subject. The students who have completed their training from are now well settled in various MNCs or well-reputed companies of India.

A little introduction to data science before joining data science training in Delhi

In this digital world, data is the new currency and the need for it is increasing as the light travel in the space. After Hadoop and other frameworks have successfully solved the problem of storage, the main focus of the business shifted to data processing. Here data science plays a key role in everything because all the ideas you see in the sci-fi Hollywood movie are turned in reality just with the help of data science.

As the world is grasping artificial intelligence very quickly, it is assumed that data science is the future of artificial intelligence and much giant industry will majorly depend on it in the near future.

Career growth of after finishing data science training in Delhi

Becoming a Data scientist is one of the latest profession which gaining popularity among the fresher graduates because it has huge demand in the It companies who majorly work in artificial intelligence. People from different backgrounds like computer science, mathematics, etc can make their career in Data science stream.

A data scientist can see career growth mainly in four main categories, data axis, a business, engineering and a product axis. The career of a data scientist can be multidisciplinary in all of these streams.

Data science training in Delhi at AIDM

Data science training in Delhi at AIDM offers a comprehensive knowledge of modern data science. We include the process of obtaining, exploring, modelling and interpreting data.

We have kept the Data Science course in Delhi very flexible as per the candidate requirements and for all the students who want to access our classes from remote areas, we provide online classroom facility for all of them. Our training sessions are divided into weekdays and weekends according to the student facility with all the latest technologies. All these facilities make our data science training in Delhi most effective and affordable.

Data science training in Delhi with 100% placement

The training and placement team of AIDM delicately works to provide 100% placement to all the students. To nurture the talent of each student they provide all assistance which includes mock interview, crystal clear subject knowledge and resume making. They try to prepare a student most suitable for the industry’s demand because thousands of other candidates would be there in the competition.

Why choose AIDM for Data science training in Delhi?

  • All our training sessions are based on live industry projects.
  • All our training modules are prepared based on current industry requirements.
  • Our training curriculum is prepared under the surveillance of our placement partners.
  • We offer weekdays and weekend basis classes which is also suitable for the professionals who are already working in the related industry.
  • Trainers having a decade of experience in the industry provide live project-based training.
  • All our trainers are certified, professionals..
  • Our labs are well equipped with the updated and latest software related to data science.
  • At data science training institute in Delhi, we offer a suitable environment for study with projectors and wifi facility.
  • We offer free personality development classes including Group discussion and mock interview facility.
  • Nationwide recognised certificate.

We have strong tie-ups with the reputed organisation and MNCs so we assure 100% job placement for our candidates in their dream organization.

Join the effective and the best Data Science course in Delhi with affordable fees structure and give your career a good hike in the IT industry.

Students are also interested in Django and Python courses, so you can also check our Django course in Delhi & python course in Delhi .


Data science Training Course Fees & Duration

TRACK Week Days Weekend Fast Track
Course Duration 40-60 Days 8 Weekends 7 Days
Hours 2 Hours Per Day 3 Hours Per Day 6+ Hours Per Day
Training Mode Classroom/ Online Classroom/ Online Classroom/ Online

Introduction of Data Science

  • What is Analytics?
  • Different types of Analytics.
  • Importance of Analytics in Business
  • Organizations sing Business Analytics

DATA & ANALYTICS

  • Data Dictionary Data Types
  • Data Handling
  • Business Data Using Excel

INTRODUCTION OF EXCEL ENVIRONMENT

  • Understanding of data calculations
  • in excel.
  • Formatting of data calculation
  • formatting.
  • MS Excel functions
  • Understanding about Sorting,
  • Filtering & Validation MS
  • Excel charts
  • Pivot Table
  • Understanding of Data Tools Panel.
  • Basics of Macro Recording

DASHBOARD DESIGNING IN EXCEL

  • Introduction to Dashboards
  • Trends ,and Scenario using charts
  • Advanced charting Techniques
  • Designing Sample Dashboard
  • using from controls Tips and Tricks
  • to enhance dashboard designing.

BASICS OF STATISTICS

  • Data types and its measures.
  • Random Varieties it’s application
  • with exercises. Probability
  • Applications with examples.
  • Probability distribution with example.
  • Various graphic representation with
  • data for analysis Continuous
  • probability distribution
  • Discrete probability distribution
  • Computing probability from
  • normal distribution Central limit
  • theorem for sampling.

INTRODUCTION TO DATA SCIENCE

  • What is Analytics and Data Science
  • Overview of Analytics and Data Science
  • Why is Analytics becoming popular now?
  • Application of Analytics in business
  • Analytics vs Data warehousing ,
  • MIS Reporting Various
  • Terminology in Analytics
  • Various Analytics Methodology
  • How Businesses are using the power of Analytics?
  • Various Analytics tools and their usage

BUSINESS STATICS AND APPLICATIONS

  • Sample V/S Population Probability Theory
  • Probability Distribution Concepts
  • Types of Distribution
  • Data Description – Numerical Measures of Central Tendency Data
  • Description – Numerical Measures of Variability
  • Inferential Statistics
  • Concepts of Hypothesis testing
  • Statistical Methods – Z/t –
  • tests , ANOVA , Correlations and
  • Chi Square

FUNDAMENTALS OF PYTHON

  • Installation of Python
  • Getting started with Python
  • History of Python Features of python
  • Variable Operators in Python
  • Reading and Writing data files to
  • PythonWorking with
  • Python data frames
  • Python Functions and Loops
  • Special utility functions
  • Merging and Sorting data

DATA IMPORTING/EXPORTING IN PYTHON

  • Concepts of Packages
  • Data Structure & Data Types
  • Importing Data from various sources
  • ( txt, dlm, excel, csv etc ) Database Input
  • Exporting data to various formats
  • Viewing Data
  • Variable & Value Labels

INTRODUCTION TO PYTHON

  • Introductory remark about python. A brief
  • History of python.
  • How python is different from other languages
  • Python versions.
  • Installing python
  • IDLE
  • How to execute python
  • Writing you first program

PYTHON BASICS

  • Python keywords and identifiers.
  • Python statements
  • Comments in python
  • Command line
  • arguments Getting use input
  • Exercise

NUMPY PACKAGE

  • What is Numpy? Importing Numpy
  • Numpy overview
  • Numpy Array creation and basic
  • operation
  • Numpy universal function
  • Selecting and retrieving data
  • Data slicing
  • Iterating Numpy Data Shape
  • Manipulation, Stacking and
  • Splitting Arrays
  • Copies and Views: no copy,
  • shallow copy, deep copy
  • Indexing : Arrays of indices,
  • Boolean Arrays

DATA MANIPULATION USING PANDAS

  • Data Alignment
  • Sorting and Ranking Summary
  • Statistics Missing Values
  • Merging data Concatenation
  • Combining DataFrames Pivot
  • Duplicates Binning

PANDAS PACKAGE

  • Importing Pandas Pandas overview
  • Object creation : Series Object ,
  • DataFrame Object View Data
  • Selecting data by Label and
  • Position Data Slicing
  • Boolean Indexing Setting Data

PYTHON ADVANCE: DATA MUGGING WITH PANDAS

  • Applying functions to data
  • Histogramming
  • String methods
  • Merge Data: Concat, Join and Append
  • Grouping and Aggregation
  • Reshaping
  • Analysing Data for missing values
  • Filling missing values: fill with constant
  • forward filling, mean Removing
  • Duplicates
  • Transforming Data

PYTHON ADVANCE: VISUALIZATION WITH MATPLOTLIB

  • Anatomy of a MatplotLib Plot
  • Matplotlib basic plots and it’s containers
  • A Matplotlib figure, it’s
  • components and
  • properties Axes and other
  • graphical objects

PYLAB AND PYPLOT

  • Data for Matplotlib Plots
  • What is a Subplot?
  • Modifying size of figures
  • Plotting routines with pyplot
  • Customizing your pyplot
  • Deleting an Axes
  • Setting up Plot Title, Axes Labels,
  • Legend,
  • Layout Showing, Saving and
  • Closing your Plot
  • Save a Plot to an image file and
  • pdf file
  • Use cla(), clf() or close

PREDICTIVE MODELLING CONCEPTS

  • How to use Predictive Modelling in
  • R Linear Regression
  • Logistic Regression Model Selection
  • Scoring
  • Predictive Modelling Techniques
  • ( Decision Trees , CART , Neural Networks ,
  • Deep Learning )
  • Different phases of Predictive Modelling
  • Business Case Study ( Predictive Modelling )

CORRELATION AND LINEAR REGRESSION

  • Correlation
  • Simple Linear Regression Multiple
  • Linear Regression
  • Building Linear Regression Model Model
  • Diagnostic and Validation Working on
  • Case Study

LOGISTIC REGRESSION

  • Moving from Linear to Logistic Regression
  • Model assumptions and odds ration
  • Building Logistic Regression Model
  • Validation of Logistic Regression Model
  • Model assessment and gains table
  • ROC curve and KS statistics Case Study
  • Time Series Forecasting
  • What are time-series? Need for forecasting
  • Trends , Seasons , Cycles
  • Basic Techniques – Averages,
  • Smoothening etc.
  • Advanced Techniques – AR Models, ARIMA etc.
  • Case Study

INTRODUCTION TO MACHINE LEARNING

  • What is Machine Learning.
  • History of Machine
  • Learning How artificial
  • intelligence relates to
  • machine learning
  • Data science vs Machine
  • Learning Fundamentals of
  • Machine Learning

MACHINE LEARNING CONCEPTS

  • Branches of Machine Learning
  • Different phases of Machine
  • learning modeling
  • Data preparation for
  • modelling Train test split
  • Evaluation of the model

SUPERVISED LEARNING

  • Classification
  • KNN
  • Logistic
  • Regression
  • Naïve
  • Bayes SVM
  • Decision Tree
  • Regression
  • Linear
  • Regression SVR
  • KNN Ridge
  • Regression
  • Model Complexity
  • Generalization
  • Performance
  • Connection between Model
  • complexity and Generalization
  • Performance Importance of feature
  • Scaling Regularization
  • Cross validation for model evaluation

EVALUATION

  • Understanding of Evaluation and model
  • selection methods Optimize the
  • performance of Machine Learning models

ADVANCED SUPERVISED MACHINE LEARNING CONCEPTS

  • Ensemble Learning
  • Learning critical problem of
  • data leakage in machine
  • learning and how to detect
  • and avoid it

UNSUPERVISED LEARNING

  • K-Means clustering
  • Recommendation Engines

UNSUPERVISED LEARNING – CLUSTERING

  • K-Means Clustering
  • Document retrieval : A case study in
  • clustering and measuring similarity
  • Un Recommending Products

UNSUPERVISED LEARNING – DEEP LEARNING

  • Meaning and importance of deep learning
  • Artificial Neural Networks
  • Introduction to Tensorflow

REINFORCEMENT LEARNING

  • INTRODUCTION
  • Reinforcement Learning
  • Overview
  • Markov Decision Process (MDP)
  • MDP - Finding Optimal Policy
  • Example of an MDP and
  • Bellman Equations

TEXT MINING

  • Introduction to Text Mining concepts
  • Finding patterns in text : text mining , text as
  • a graph Natural Language Processing (NLP)
  • Sentiment Analysis with R Word Cloud
  • analysis using R
  • Application of Social media analytics
  • Collecting twitter data with twitter API
  • Feature engineering with text data
  • Fine tuning the models using hyper
  • parameters, grid search, piping etc.
  • Case Study


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You can earn up to 15,000 to 40,000/- per month in the beginning. The salary will vary upon your skill sets. It may go up to in Lacs if you hold that level of skill set

Yes, we do provide 100% job placements after completion of any course.

There is no such eligibility area required for a digital marketing course.

As per our records almost all the candidates got the job or promotions respectively within one month after they completed the course. Through our course you can explore three-way opportunities as you can simultaneously do your regular job and work as a freelancer side by side and also can earn money through affiliate marketing.

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