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Data Science Classroom Training Course

Data Science Classroom Training

  (120 Reviews)
      1200 Registered Users

Elevate Your Data Science Game with NearLearn
Data science is an ever-evolving field, and with NearLearn, you can stay ahead of the game. Our comprehensive data science course in Bangalore is designed to help you unlock your potential and become an expert in this field.

Data Science Classroom Training in Bangalore
52,000 + GST
47,000 + GST

(0% Interest on EMI)

Earn certificates you complete courses covering the whole data science work process. Regardless of whether you're simply beginning or a prepared expert, you'll improve your aptitudes in data manipulation, data visualization, statistics, AI, and more Quicken your profession with the selective Data Scientist Master's Program as a team with Nearlearn. Experience world-class preparation by an industry leader on the most sought after Data Science and Machine learning aptitudes. Gain hands-on exposure to key advancements including R, SAS, Python, Tableau, Hadoop, and Spark. Become a specialist Data Scientist today.

With Nearlearn introduces understudies with incorporated blended learning, making them specialists in Artificial Intelligence and Data Science. Endless supply of this current Master's Program, Participant will receive the certificate from Nearlearn in the Data Scientist courses on the successful learning path. These certificates will vouch for your aptitudes as a specialist in Data Science. You will likewise get the accompanying:
Placements in Top IT MNC companies
Industry-perceived Master's Certificate from NearLearn
Data Scientist is probably the hottest profession. Nearlearn predicts the interest for Data Scientists will ascend by 28% by 2020. Nearlearn’s Data Scientist Master's Program urges you to master abilities including statistics, hypothesis testing, Data mining, clustering, decision trees, linear and logistic regression, data wrangling, data visualization, regression models, Hadoop, Spark, PROC SQL, SAS Macros, suggestion motor, administered, and unaided learning and then some.
This Data Scientist Master's Program covers broad Data Science training, joining Classroom educator drove classes and self-paced learning co-created with Nearlearn. The program finishes up with a capstone project intended to strengthen the learning by building a genuine industry product enveloping all the key angles learned all through the program. The abilities concentrated on right now help set you up for the job of a Data Scientist.

  • Gain a top to bottom comprehension of data structure and data manipulation
  • Comprehend and utilize linear and non-linear regression models and classification strategies for data analysis
  • Acquire an in-depth comprehension of supervised and unsupervised learning models, for example, linear regression, clustering, dimensionality decrease, K-NN, and pipeline
  • Perform logical and specialized computing utilizing the SciPy package and its sub-packages, for example, Integrate, Optimize, Statistics, IO, and Weave
  • Increase ability in scientific figuring utilizing the NumPy and Scikit-Learn bundles
  • Comprehend the various segments of the Hadoop ecosystem
  • Figure out how to function with HBase, its design, and data storage, learning the contrast among HBase and RDBMS, and use Hive and Impala for dividing
  • Comprehend MapReduce and its qualities, also, to figure out how to ingest data utilizing Sqoop and Flume
  • Master the ideas of recommendation engine and time series displaying and gain down to mastery dominance over standards, algorithms and utilizations of AI
  • Figure out how to examine information utilizing Tableau and become capable of building intuitive dashboards.

    • The Data Science job requires an amalgam of experience, data science information, and the right apparatuses and technologies. It is a strong career decision for both new and experienced experts. Aspiring experts of any instructive foundation with a logical mood are generally fit to seek after the Data Scientist Master's Program, including:

  • IT Professionals
  • Analytics Managers
  • Business Analysts
  • Banking and Finance Professionals
  • Marketing Managers
  • Inventory Network Managers
  • Beginners or Fresh Graduates in Bachelors or Master's Degree

    • COURSES CURRICULUM

    Lecture1.1 Key Elements of Machine Learning & Data Science & differences between them

    Lecture1.2 Data Warehousing

    Lecture1.3 Business Intelligence

    Lecture1.4 Data Visualization

    Lecture1.5 Data Mining

    Lecture1.6 Machine Learning

    Lecture1.7 Artificial Intelligence

    Lecture1.8 Cloud Computing

    Lecture1.9 Big Data

    Lecture2.1 What is Machine Learning (ML)?

    Lecture2.2 How machines learn

    Lecture2.3 Basics of Classification, Regression and Clustering algorithms

    Lecture2.4 Creating your first Prediction Model

    Lecture2.5 Training & Model Evaluation

    Lecture2.6 Choosing Machine Learning Algorithm

    Lecture3.1 Operators, Operands and Expressions

    Lecture3.2 Python Data Types

    Lecture3.3 Conditional statements in Python

    Lecture3.4 Loops in Python

    Lecture3.5 Lists and dictionaries and Tuples

    Lecture3.6 Programming practice in Python

    Lecture3.7 Iterators & Generators

    Lecture3.8 File Handling in Python

    Lecture3.9 Modules and Libraries

    Lecture3.10 Classes and Objects

    Lecture3.11 String Formatting in Python

    Lecture3.12 Decorators, Context Managers, Regular Expressions

    Lecture3.13 List and Dictionary Comprehensions

    Lecture3.14 Lambda and Argument Passing

    Lecture3.15 Multiple Inheritance

    Lecture4.1 Linear Algebra (Vectors, Matrix, Eigen Values)

    Lecture4.2 Probability and Statistics

    Lecture4.3 Hypothesis testing

    Lecture4.4 Optimization

    Lecture5.1 Introduction to Numpy

    Lecture5.2 Arrays, Matrices,

    Lecture5.3 Various operations on arrays and matrices

    Lecture5.4 Introduction to Pandas

    Lecture5.5 Reading csv and matlab files

    Lecture5.6 Data frame object manipulation in python

    Lecture5.7 Various operations on data frame

    Lecture5.8 Visualization using Matplotlib

    Lecture5.9 Scatter plots, line plots etc on a given data

    Lecture5.10 Advance visualization using Seaborn

    Lecture5.11 Histograms, heatmaps, box plots etc using seaborn

    Lecture6.1 Basic Functionalities of a data object

    Lecture6.2 Merging of Data objects

    Lecture6.3 Concatenation of data objects

    Lecture6.4 Types of Joins on data objects

    Lecture6.5 Exploring a Dataset

    Lecture6.6 Analysing a dataset

    Lecture6.7 Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()

    Lecture6.8 GroupBy operations

    Lecture6.9 Aggregation

    Lecture6.10 Concatenation

    Lecture6.11 Merging

    Lecture6.12 Joining

    Lecture6.13 Data Collection &Preparation

    Lecture6.14 Data Mugging

    Lecture6.15 Outlier Analysis

    Lecture6.16 Missing value treatment

    Lecture6.17 Feature Engineering

    Lecture6.18 Data Transformation

    Lecture6.19 Normalization vs Standardization

    Lecture6.20 Creating Dummies

    Lecture6.21 Dimensionality Reduction

    Lecture6.22 Principal ComponentAnalysis

    Lecture7.1 Confidence Interval

    Lecture7.2 Student’s t distribution

    Lecture7.3 Binomial Distribution

    Lecture7.4 A/B Testing

    Lecture7.5 Hypothesis Testing

    Lecture7.6 t-Tests

    Lecture7.7 ANOVA

    Lecture7.8 Chi-square test

    Lecture7.9 KNN

    Lecture7.10 PCA

    Lecture7.11 Categorical Variables

    Lecture7.12 R Square

    Lecture8.1 Linear & Logistic and Regression Techniques

    Lecture8.2 Problem of Collinearity

    Lecture8.3 WOE and IV

    Lecture8.4 Residual Analysis

    Lecture8.5 Heteroscedasticity

    Lecture8.6 Homoscedasticity

    Lecture9.1 Supervised Machine Learning algorithms

    Lecture9.2 Linear Regression

    Lecture9.3 Multi Feature

    Lecture9.4 Logistic Regression

    Lecture9.5 2 Class and Multi class

    Lecture9.6 Decision/ Classification

    Lecture9.7 Trees

    Lecture9.8 Ensemble

    Lecture9.9 Models

    Lecture9.10 Bagging

    Lecture9.11 Boosting

    Lecture9.12 Random Forest

    Lecture9.13 K-Nearest Neighbours (KNN)

    Lecture9.14 Naive Bayes

    Lecture9.15 Introduction to Neural Network (DeepLearning)

    Lecture9.16 Feed Forward Neural Network

    Lecture9.17 Forward Propagation

    Lecture9.18 Backward Propagation

    Lecture9.19 Support Vector Machine

    Lecture9.20 Unsupervised Machine Learning algorithms

    Lecture9.21 Clustering with K-means Clustering

    Lecture9.22 Bias-Variance Tradeof

    Lecture9.23 Regularization

    Lecture9.24 Parameter tuning & grid search optimization

    5000+ Handwritten Digit Recognition Problem

    4000+ email spam detection problem

    Image compression Problem

    Flower species classification problem

    Titanic Survivor classification problem from kaggle

    Fifa ranking dataset from kaggle

    Profit Prediction Problem

    Business Case of whether a chip will be accepted or not

    Business case of clustering from dataset

    Wine classification dataset and problem from Kaggle

    Variety of Problems from Kaggle Competition Data Sets

    Reviews

    studant

    Shubham

      (2 Review)

    I have enrolled for NearLearn Machine Learning with Python Course Training. The Session was very Nice. The trainer is knowledgeable and his skills of explaining the concepts are awesome.

    studant

    Sneha Deshpande

      (2 Review)

    I am satisfied with the Machine Learning workshop coducted by NearLearn. The instructor explained course objectives clearly. Made class enjoyable as well as educational.

    studant

    Rudranee

      (2 Review)

    Machine Learning Training session was good.I am satisfied about this course.The training experience will be useful in my work. Course explanation was very clear and understandable.

    studant

    Ketan

      (2 Review)

    The session was very enjoyable as well as educational. This training experience will be very useful in my work. I would really recommend this instructor. Thanks

    FAQs

    Our course includes case studies and projects that simulate real-world challenges. You will work on projects that will help you apply the concepts you learn during the course.

    No prior coding knowledge is required. We start from the basics and gradually move towards advanced concepts, making it easy for you to understand.

    You will acquire the skills and knowledge required to become an expert in data science. You will have hands-on experience working on real-world challenges, making you stand out in the job market.

    Yes, you will have lifetime access to our course materials. You can revisit the materials to brush up on your skills.

    Yes, you will receive a certificate upon completion. We are well-known, especially for our data science Training Course Certification in Bangalore & our certificate is recognized by industry experts and will help boost your career in data science.

    Yes, we provide job placement assistance and interview preparation to all our students. We will help you prepare your resume and provide tips for your job interview.

    Register Now

    • Data Science Training - Instructor LED Classroom Classes
    52,000 + GST47,000 + GST

    Upcoming Classroom Trainings

    October 1st | Weekdays | 8 Weeks | 09:30AM TO 10:30AM

    October 12th | Weekends | 12 Weeks | 9:00AM TO 12PM

    October 14th | Weekdays | 8 Weeks | 09:30AM TO 10:30AM

    October 26th | Weekends | 12 Weeks | 10:00AM TO 01PM

    November 5th | Weekdays | 8 Weeks | 09:30AM TO 10:30AM

    November 9th | Weekends | 12 Weeks | 9:00AM TO 12PM

    November 19th | Weekdays | 8 Weeks | 09:30AM TO 10:30AM

    November 23rd | Weekends | 12 Weeks | 10:00AM TO 01PM

    December 3rd | Weekdays | 8 Weeks | 09:30AM TO 10:30AM

    December 14th | Weekends | 12 Weeks | 9:00AM TO 12PM

    December 17th | Weekdays | 8 Weeks | 09:30AM TO 10:30AM

    December 28th | Weekends | 12 Weeks | 10:00AM TO 01PM

    View All Upcoming Trainings

    Training Features

    Data Science Experts
    Our trainers are not just teachers; they are experts in the field of data science with hands-on experience in the industry. They will inspire and guide you to reach new heights in your data science career.

    Real-world challenges
    We believe in challenging our students with real-world problems. Our course includes case studies and projects that simulate real-world challenges and provide practical learning opportunities.

    Flexibility without Compromise
    We know everyone has a busy schedule, so we offer weekday and weekend batches. Our flexible schedule will ensure you can complete the course without compromising your work or personal life.

    Interactive, Engaging, and Fun Learning
    Our trainers make learning interactive, engaging, and fun. They will ensure that you understand the concepts by providing examples and interactive sessions to keep you engaged throughout the course.

    Lifetime Access to a Treasure Trove of Knowledge
    We provide lifetime access to our course materials, which include videos, assignments, projects, and updates. Our treasure trove of knowledge will allow you to stay updated and learn quickly.

    Unlock Your Dream Job
    We provide job placement assistance and interview preparation to all our students. Our ultimate goal is to help you unlock your dream job and provide you with the resources to achieve it.