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Advanced Data Science Course with Artificial Intelligence in Marthahalli, Bengaluru.

Social Prachar is one of the Top Data Science Training Institute in Maratahalli, Bangalore with Placement assistance. We will provide Advanced Data Science training includes Python, Machine Learning, Statistics, Deep learning, NLP etc with Real time trainers, client case studies and live Real world projects. Data Science course with Certification from Social Prachar will help to the Candidates to get in-depth knowledge of Data Science by laying the strong foundation for the career. 700+ Trainees Rated us Best Data science Training in bengaluru.

Data Science is the most awaited and promising career in the 21st century with 2,00,000+ Job opportunities by 2021 with minimum 4LPA to 15LPA salaries.

  • We provide both Data science Training in bengaluru Classroom & Online course programs.
  • Social Prachar provides Data science Training in Bangalore with Statistics, Python , Machine Learning, Deep learning , Natural Language Process, Artificial Intelligence with 5+ Real time Projects.

New Batch Starts on 8th April 2021 9AM (Classroom/Offline)  – Enroll now

Offer valid till April 30th 2021

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    Very Delighted to share that Social Prachar has been awarded as the Best Academy of the Year 2019 – 2020 @7th Asian Education Summit, Mumbai Presented by Juhi Chawla, former Miss India

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    A NOTE FROM OUR CEO

    Dear Engineers please upskill fast enough to meet ever-changing market needs. UpGrading & UpSkilling to the market requirements is the need of the Hour. A Wipro study reveals that 75% organizations find the need to upgrade IT infrastructure. At a Time when most organizations take digital transformation plunge, AI, Data Science, Cloud computing, Cyber security & UI/UX related jobs remain in HIGH DEMAND. Your Flexibility and skills will be a HUGE asset to you post covid19 job market.

    Learn TODAY ~ Lead TOMORROW

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    Data Science AI Course Training in Bengaluru - Key Highlights

    • Award Winning Training Center with 5000+ Successful career Transitions in last 5 years

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    • There is a wide variety of career opportunities available for Data Science Professionals because Data Scientists are required in different areas like Data Analytics , Data Research, Big Data and Data Management and more.
    • Data Science Jobs has high range of Salaries. Minimum average salary ranges are from 5 Lakhs to 30 lakhs per annum.
    • We are providing Data science Training in bengaluru with Well Expert Trainers from industry with Budget friendly price.

     

    Data Science Training in Bengaluru

    Master Data Science with Certification

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    Data Science Course Highlights

    Realtime Expert Trainers
    Dedicated portal for Practice
    Job Support
    Weekly Assignments
    Live Projects to work

    Who to Join Data Science course

    IT Professionals,
    Data Analysts, Business Analysts,
    Functional Managers,
    Graduates, Post Graduates
    Also, anyone having interest in Data Science is free to attend our workshop.

    The Indian Analytics industry is expected to grow at a CAGR of 26% till 2025 and is expected to capture 32% of the global data market.

    IBM Predicts demand for Data Scientists will rise by 28%

    Advance your Career with our Specialization Data Science Course.

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    Data Science Job Roles Available

    Jr Data Scientist
    Data Analyst
    Business Intelligence Analyst
    Data Mining Engineer
    Data Architect
    Senior Data Scientist
    Data Engineer

    Data Science Job Market in 2021

    Sexiest Job of 21st Century. Data Scientist is the Sexiest Job of the 21st Century by Harvard university
    8,000+ unique job postings for data scientists each month.
    5,00,000+ Skilled Data Scientists required by the End of 2020.
    Salaries range from 4LPA to 15LPA.
    GlassDoor ranked it top place for popular jobs

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    Data Science course Curriculum

    1. What is Data Science? – Introduction.
    2. Importance of Data Science.
    3. Demand for Data Science Professional.
    4. Brief Introduction to Big data and Data Analytics.
    5. Lifecycle of data science.
    6. Tools and Technologies used in data Science.
    7. Installing Python IDE
    8. Installing Python Environments like Jupyter, Pycharm, Spyder etc.
    9. Installing Packages – Loading and Unloading Packages

     

    INTRODUCTION TO STATISTICS

     

    Basic business statistics How businesses use statistics |The basic vocabulary of statistics |The types of data used in business

    Organizing and Visualizing Data

     

    The sources of data used in business | To construct tables and charts for numerical data | To construct tables and charts for categorical data | The principles of properly presenting graphs

     

    Numerical Descriptive Measures

     

    To describe the properties of central tendency, variation, and shape in numerical data | To construct and interpret a boxplot | To compute descriptive summary measures for a population | To compute the covariance and the coefficient of correlation

     
    Basic Probability and Terms

    Basic probability concepts | Conditional probability | To use Bayes’ Theorem to revise probabilities | Various counting rules

     
    Probability Distributions Types of Distributions like Discrete and Continuous | Functions of Random Variables | Probability Distribution Graphs
    Sampling and Sampling Distributions

    To distinguish between different sampling methods | The concept of the sampling distribution | To compute probabilities related to the sample mean and the sample proportion | The importance of the Central Limit Theorem

     

    Confidence Interval Estimation

     

    To construct and interpret confidence interval estimates for the mean and the proportion | How to determine the sample size necessary to develop a confidence interval estimate for the mean or proportion | How to use confidence interval estimates in auditing

     
    Fundamentals of Hypothesis Testing Null and Alternate hypothesis | One sample Tests | Test statistic and critical values | Possible errors in testing | p-value approach | t and z-tests | Testing on proportions

     

    PYTHON PROGRAMMING

     

    Introduction to Python

    Installation | Python Basics | Spyder IDE | Jupyter Notebook  |  Floats and Strings Simple Input & Output  |  Variables  | Operators | Single and Multiline Comments |

    Taking input from user
    Data Structures List | Strings | Tuple | Dictionary | Sets and their examples
    Conditional Statements If | if-else | if-elif-else | nested if else
    Loops For | while | nested loops| loop control statements
    Functions Creating user defined Functions | Function arguments like- Required, Keyword, Default and variable-length | Scope of variables in creating functions | Anonymous Functions – Lambda
    Exception and File handling Exception Handling | Raising Exceptions | Assertions  | Files I/O
    Object Oriented Programming Introduction | Class & Instance Attributes | Properties vs getters and setters | Inheritance | Abstract Classes

     

    DATA ANALYSIS & VISUALIZATION WITH PYTHON

     

    Numpy module Basics of numpy | creating multidimensional arrays | Numpy operations
    Pandas Introduction to Pandas  |  IO Tools  |  Pandas – Series and Dataframe and their wide range of functionalities
    Matplotlib, Seaborn & Word Cloud

    Graphical representation of data using various plots like bar plots, Pie plot, Histogram, Scatter plot, Box plot etc.|

    Creating word clouds with text data
    Scikit learn Introduction to SciKit Learn | Load Data into Scikit Learn |  Run Machine Learning Algorithms Both for Unsupervised and Supervised Data  |  Supervised Methods: Classification & Regression  |  Unsupervised Methods: Clustering, Gaussian Mixture Models  |  Decide What’s the Best Model for Every Scenario
    Data Transformations

    Merge, Rollup, Transpose and Append | Smoothing | Aggregation | Normalization | Attribute construction

     
    Feature Engineering Missing value Imputation | Outlier Analysis and Treatment | Binning | Creating dummy variables | feature scaling | Extracting Date | Log Transformation | Feature split | Label Encoding | One-Hot Encoding

     

     

    INTRODUCTION TO MACHINE LEARNING

     

    Introduction What is Machine Learning?  | End-to-end Process of Investigating Data Through a Machine Learning Lens |  Evolution and Trends  |  Application of Machine Learning  |  Best Practices of Machine Learning
    Machine Learning Methods Supervised | Unsupervised
    Machine Learning Algorithms Classification  |   Regression  | Time Series |  Collaborative Filtering  |  Clustering | Principal Component Analysis

     

    MACHINE LEARNING APPLICATIONS

     

    Linear Regression Implementing Simple & Multiple Linear Regression |  Making Sense of Result Parameters  |  Model Validation  |  Handling Other Issues/Assumptions in Linear Regression: Handling Outliers, Categorical Variables, Autocorrelation, Multicollinearity, Heteroskedasticity  |  Prediction and Confidence Intervals  |  Use Cases
    Logistic Regression

    Implementing Logistic Regression| Making Sense of Result Parameters: Wald Test, Likelihood Ratio Test Statistic, Chi-Square Test | Goodness of Fit Measures |  Model Validation: Cross Validation, ROC Curve, Confusion Matrix  |  Use Cases

     
    Decision Tree Implementing Decision Trees |  Homogeneity  |  Entropy Information Gain  |  Gini Index  |  Standard Deviation Reduction  |  Vizualizing & Prunning a Tree  |  Implementing Random Forests using Python  |  Random Forest Algorithm  |  Important hyper-parameters of Random Forest for tuning the model  |  Variable Importance  |  Out of Bag Errors
    Naïve Bayes Bayes Theorem | Gaussian Naïve Bayes & its implementation | Multinomial Naïve Bayes, Count vectorizer, TF-IDF Vectorizer and its use cases on Text Classification
    K-Nearest Neighbors Concept of nearest neighbors | Euclidean Distance | Use Cases of KNN Classifier & Regressor
    Support Vector Machine Introduction to support vectors | Concept of hard & soft margins | slack variable | Lagrangian Primal & Dual | Kernel Trick | Use Cases
    Time Series Introduction | Components of Time Series | Stationarity | ACF | PACF | ARIMA model for forecasting | Use Cases
    K-Means Clustering Clustering concept | Finding optimal number of clusters | Use Cases
    Hierarchical Clustering Agglomerative & Divisive Clustering | Dendrograms | Linkage Matrix like Single, Complete & Average | Use Cases
    Ensemble Techniques Bagging | Boosting | Stacking | Regularization | Different cross-validation techniques used to treat Over fitting and Under fitting in machine learning models
    Principal Component Analysis Concept of Dimensionality reduction | Eigen values | Eigen Vectors | Use Cases

     

    PROJECTS

     

    PROJECT 1

    Title: Real Estate Price Prediction using Linear Regression

    Industry: Real Estate

    Description: The goal of this Use-case is to make property price predictions using Real Estate data. The dataset contains the of the price of apartments and various characteristics of the property. Based on this data, decide on the price of new properties.
    PROJECT 2

    Title: Loan Prediction using Logistic Regression & Decision Tree

    Industry: Finance

    Description: The goal is to build a classification model to predict if a loan is approved or not. Dataset contains demographic information like age, income etc. of various customers. Based on this, predict for a new customer whether loan will be approved or not
    PROJECT 3

    Title: Recommendation for Movie, Summary

    Topics: This is real world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provide data-driven recommendations. This project involves understanding recommender systems, information filtering, predicting ‘rating’, learning about user ‘preference’ and so on. You will exclusively work on data related to user details, movie details, and others.   

     
    PROJECT 4

    Title: Handwritten digit recognition using Decision Tree &Random forest

    Description: Dataset contains various pixel values of handwritten digits between 0 to 9. The goal is to build a classification model to predict the digit from its pixel values.
    PROJECT 5

    Title: Amazon fine food review-Sentiment Analysis

    Industry: Amazon

    Description: The analysis is to study Amazon food review from customers and try to predict whether a review is positive or negative. The dataset contains more than 500k reviews with number of upvotes and total votes to those comments

     

      DEEP LEARNING ALGORITHMS & APPLICATIONS

     

    Neural Networks Understanding Neural Networks  |  The Biological Inspiration  |  Perceptron Learning & Binary Classification  |  Backpropagation Learning  |  Learning Feature Vectors for Words  |  Object Recognition
    Keras Keras for Classification and Regression in Typical Data Science Problems  |  Setting up KERAS  |  Different Layers in KERAS  |  Creating a Neural Network Training Models and Monitoring  |  Artificial Neural Networks
    Tensorflow Introducing Tensorflow  |  Neural Networks using Tensorflow  |  Debugging and Monitoring  |  Convolutional Neural Networks
    Recurrent Neural Networks Introduction to RNN | RNN long & short term dependencies | Vanishing gradient problem | Basic LSTM | Step by step walk through LSTM | Use cases

    PROJECT 1

    ANN ON KERAS

    Title: Credit Default using ANN on Keras

    Industry: Finance

    Description: This research aimed at the case of customers’ default payments in Taiwan. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification – credible or not credible clients.

    PROJECT 2

    CNN ON KERAS & TENSORFLOW

    Title: Handwriting/Facial recognition using CNN on TensorFlow & Keras

    Industry: Pattern Recognition

    Description: This project will help build a model using Convolutional Neural Network to recognize handwriting/facial images

    PROJECT 3

    LSTM

    Title: Google stock price prediction

    Industry: Finance

    Description: Dataset contains dates, volume, open, close, high and low prices of stocks. Based on this build a LSTM model to predict current and future stock price

    PROJECT 4

     

    Title: Traffic Signs Recognition

    Description:

    In self-driving cars in which the passenger can fully depend on the car for traveling. But to achieve level 5 autonomous, it is necessary for vehicles to understand and follow all traffic rules.

    In the world of Artificial Intelligence and advancement in technologies, many researchers and big companies like Tesla, Uber, Google, Mercedes-Benz, Toyota, Ford, Audi, etc are working on autonomous vehicles and self-driving cars. So, for achieving accuracy in this technology, the vehicles should be able to interpret traffic signs and make decisions accordingly.

    Highlights: This Python project is about building a deep neural network model that can classify traffic signs present in the image into different categories. With this model, we are able to read and understand traffic signs which are a very important task for all autonomous vehicles.

     

    Computer Vision with Python:

    • Introduction to OpenCV
    • Core Operations
    • Image Processing in OpenCV
    • Feature Detection and Description
    • Video Analysis
    • Machine Learning
    • Object Detection
    • OpenCV-Python Bindings

    NLP with Python:

    • Introduction to NLTK
    • Tokenizing words and Sentences with NLTK
    • Stop words and Stemming with NLTK
    • Part of Speech Tagging with NLTK
    • Chunking & Chinking
    • Lemmatizing with NLTK
    • Wordnet with NLTK
    • Converting words to Features
    • Text Classification with NLTK
    • Combining Algorithms with NLTK
    • Creating a module for Sentiment Analysis
    • Twitter Sentiment Analysis with NLTK
    • Named Entity Recognition with Stanford NER Triggers
    • Testing NLTK and Stanford NER Triggers for Accuracy and Speed

     

    SQL (Structured Query Language)

    • Introduction to SQL
    • SQL Select Statements
    • Execute a basic SELECT statement
    • Restricting and Sorting Data
    • Limit the rows retrieved by a query
    • Sort the rows retrieved by a query
    • Single-Row Functions
    • Describe various types of functions available
      in SQL
    • Use character, number, and date functions in SELECT statements
    • Describe the use of conversion functions
    • Displaying Data from Multiple Tables
    • Write SELECT statements to access data from more than one table using equality and nonequality joins
    • View data that generally does not meet a join condition by using outer joins
    • Join a table to itself by using a self join
    • Aggregating Data Using Group Functions
    • Identify the available group functions
    • Describe the use of group functions
    • Group data using the GROUP BY clause
    • Include or exclude grouped rows by using the HAVING clause
    • Subqueries
    • Manipulating Data
      • Describe each DML statement
      • Insert rows into a table
      • Update rows in a table
      • Delete rows from a table
      • Merge rows in a table
      • Control transactions
    • Creating and Managing Tables
    • Including Constraints
    • Describe constraints
    • Create and maintain constraints

    Frequently Asked Questions

    What about Your Data Science Course Details ?

    We are Providing 3 Month and 6 Month Curriculums on Data Science includes 2 Real Time Projects, MNC certifications, Job support, Community Meetups, Bootcamps etc to Build your Career into data science field.

    What type of Training Modes You Provide?

    Right Now we are offering Daily, Weekend, Online modes of Training. Check our Upcoming Batches page for the latest coming up Classroom, Online and weekend batch schedules.

    Who are Eligible to Learn Data Science?

    • IT Professionals,
    • Data Analysts,
    • Business Analysts,
    • Functional Managers,
    • Graduates,
    • Post Graduates
    • Also, anyone having interest in Data Science

    What skills do I need to Master Data Science?

    You need to get skilled on following, in order to take a deep dive in this field.

    • Statistic and probability
    • Algorithms
    • Programming Languages (Java, Scala ,SQL, R, Python)
    • Data mining
    • Machine learning
    • Deep Learning

    What is the Data Science Job Market Now ?

    • Sexiest Job of 21st Century. Data Scientist is the Sexiest Job of the 21st Century by Harvard university
    • 6,000 unique job postings for data scientists each week.
    • 3,00,000+ Skilled Data Scientists required by the End of 2018.
    • salaries range from Min 3L per Annum to 20 L per Annum.
    • GlassDoor ranked it top place for popular jobs

    Do You Provide Job Placements ?

    Yes, We have a Dedicated HR team for Placement support Like Resume Preparations, Mock Interviews, Main Interviews, Company Tie-ups etc

    What Type of Certifications You Provide After the course ?

    We Provide Data Science Mastery Certificate from Social Prachar and Two More MNC certifications. Get In touch with the Team for more details about all our certifications.

    Do You Provide Hands-on Experience ?

    Yes All the data science Trainees need to work on min 2 Real Time projects for Better understanding of Real Time Data Science Methods and Techniques.

    What about Trainer Faculty experience ?

    Our Trainer Faculty is from Top MNC’s who has 10+ years of Experience with Real time clients. They will Mentor you all the way till you will get confidence on the subject. We have Community meetups,Boot Camps,workshops etc where you can meet the industry experts.

    What is data Science ?

    Data Science is the New Buzz word is the Market Right Now !

    Data Science involves using automated methods to analyze massive amounts of data and to extract knowledge from them. A Data Scientist is someone who is better at Statistics than any software Engineer and better at Software Engineering than any Statistician. They are responsible for discovering insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals.

    I am 2018 Fresher Am i Eligible to Learn Data Science course?

    Yes, You are Eligible to Learn Data Science Course If you are Okay to Learn Programming and Good with Mathematics and Business Knowledge

    What are the Modules you cover in Data Science Course ?

    We will Start from basics Like Introduction to Data science,R,Python,Deep Learning,Statistics,Machine Learning, Hadoop, Spark, SQL Etc with Two Real Time Projects at the End for Better Understanding of Data Science.

    I have 3+ Years IT experience, am i Eligible to Learn Data Science Course?

    Yes, Undoubtedly Your Profile is Eligible to Learn Data Science course.

    I'm from EEE background , can I go for data science , I don't have basics of any programming language

    Yes, you can if you are willing to learn python programming and good with mathematics and statistics.

    I don't have any engineering background can I still learn data Science.

    All Bsc, BCom, BTech, MBA, MTech MCA are eligible to learn Data Science.

    Will u explain about the whole data science course including Python, R lang etc....?

    Yes, Data Science ,R,Python,and tablueau, etc

    Will this be best option for MBA finance students? How will it be helpful for finance students??

    Now its booming so anyone who completed Btech/MBA/Degree etc can learn, but only if you are willing to learn programming.

    I'm weak at Mathematics, Can I opt Data Scientist as my career ?

    Yes you can opt but need to work a lot on maths. Our team will provide you the required material to overcome.

    I'm from Pharmacy background. Please suggest me whether am I applicable to do this course?

    Yes, IF, You have less than 0-2 years in Pharmacy you can learn. And also if u are willing to learn programming you can learn data science.

    I'm from mechanical background, but i want to learn data science , is it possible to understand without knowledge in languages like c , java knowledge

    Yes, you can learn easily. Our curriculum starts from scratch, so you can learn without much confusions.

    Hello am from medical science field am I eligible for this course

    No. If its just for learning no problem. But if u need to make a career into data science, it will be little tough.

    Is there any minimum percentage criteria of graduates for jobs in data science

    No Minimum criteria but need to have good knowledge on Mathematics.

     

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