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Data Science course in Bangalore with 100% Placement

Experience 100% live training with industry experts. Sign up in Skillslash’s Data Science course in Bangalore, work in real-time projects and gain globally acknowledged certifications.

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350 Hrs

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15+ Projects

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15 May 2022

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Starting 5,801/month

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Data science course in Bangalore

Data Science Course In Bangalore

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Enroll in our Data Science course and get industrial training. Also, get a chance to working directly with top AI companies.

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Self-design your Data Science course with respect to your career goals. Also, take the training under expert guidance.
Data Science Course In Bangalore

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Get a 100% job guarantee in top Data Science and AI firms. Also, receive guidance on resume building and interview preparation.
Data Science Course In Bangalore

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This Data Science course in Bangalore is for professionals with at least 1 year of work experience. Besides, no prior programming experience is required.

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₹ 59,000 + GST

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complete required modules. for projects

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Under Skillslash’s best Data Science course, work on collaborative projects with top AI companies. Also, along with core Data science, AI and ML training, work on real-time Data science projects. Besides, get project experience certificate to land in your dream data science job roles.

  • Enroll in Bangalore’s best Data Science course and gain practical experience. Also, work on real-time Data Science projects with AI Companies.
  • Sign up in one of the Best Online Data Science courses in India. Besides, learn the nuances of projects from scratch to the deployment level.
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Sign up in India’s best Data Science course in Bangalore with 100% job guarantee. The Data Science courses are designed with an application-based learning approach. Furthermore, the extensive Artificial Intelligence and Data Science Syllabus focuses on helping learners build relevant experience. Especially, in the technologies they upskill to prove expertise.

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Syllabus

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Program Highlights
  • 350+ Live sessions
  • 15+ Industry Projects
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Edit

Source code Vs bytecode Vs machine code, Compiler Vs Interpreter, C/C++, Java Vs Python.

Different type of code editors in python, Introduction to Anaconda and IDEs

Variable Vs Identifiers, Strings Operators Vs Operand, Procedure oriented Vs Modular programming.

Measures of Central Tendency & dispersion, Inferential statistics and Sampling theory.

Edit

Source code Vs bytecode Vs machine code, Compiler Vs Interpreter, C/C++, Java Vs Python.

Different type of code editors in python, Introduction to Anaconda and IDEs

Variable Vs Identifiers, Strings Operators Vs Operand, Procedure oriented Vs Modular programming.

Measures of Central Tendency & dispersion, Inferential statistics and Sampling theory.

Edit
  • Introduction to Probability Principles
  • Random Variables and Probability principles
  • Discrete Probability Distributions - Binomial, Poisson etc
  • Continuous Probability Distributions - Gaussian, Normal, etc
  • Joint and Conditional Probabilities
  • Bayes theorem and its applications
  • Central Limit Theorem and Applications
  • Elements of Descriptive Statistics
  • Measures of Central tendency and Dispersion
  • Inferential Statistics fundamentals
  • Sampling theory and scales of measurement
  • Covariance and correlation
  • Basic Concepts - Formulation of Hypothesis, Making a decision
  • Advanced Concepts - Choice of Test - t test vs z test
  • Evaluation of Test - P value and Critical Value approach
  • Confidence Intervals, Type 1 and 2 errors
  • Ingest data
  • Data cleaning
  • Outlier detection and treatment
  • Missing value imputation
  • Capstone project for Business Analysis
Edit
  • Types of Learning - Supervised, Unsupervised and Reinforcement
  • Statistics vs Machine Learning
  • Types of Analysis - Descriptive, Predictive, and Prescriptive
  • Bias Variance Tradeoff - Overfitting vs Underfitting
  • Correlation vs Causation
  • Simple and Multiple linear regression
  • Linear regression with Polynomial features
  • What is linear in Linear Regression?
  • OLS Estimation and Gradient descent
  • Model Evaluation Metrics for regression problems - MAE, RMSE, MSE, and MAP
  • Introduction to Classification problems
  • Logistic Regression for Binary problems
  • Maximum Likelihood estimation
  • Data Imbalance and redressal methodology
  • Up sampling, Down sampling and SMOTE
  • Introduction to Unsupervised Learning
  • Hierarchical and Non-Hierarchical techniques
  • K Means Algorithms - Partition based model for clustering
  • Model Evaluation metrics – Clustering
  • Introduction to KNNs
  • KNNs as a classifier
  • Non-Parametric algorithms and Lazy learning ideology
  • Applications in Missing value imputes and Balancing datasets
  • Introduction to regularization
  • Understanding ridge regression
  • Working with Lasso regression
  • Tackling multicollinearitywith regression
  • Nonlinear models for classification
  • Intro to decision trees
  • Why are they called Greedy Algorithms?
  • Information Theory - Measures of Impurity
  • Introduction to Bagging as an Ensemble technique
  • Bootstrap Aggregation and Out of Bag error
  • Random Forests and its Applications in Feature selection
  • How Bagging overcomes the overfitting problem?
  • Scent and Boosting
  • How Boosting overcomes the Bias - Variance Tradeoff
  • Gradient Boosting and Xgboost as regularised boosting

  • Introduction to Expectation— Maximization Algorithms

  • The kernel tricks

  • Linear, Polynomia, and RBF kernels, SVMs for regression and classification

  • Applications in Multiclass classification.

  • Naive Bayes for Text classification

  • Bag of words and TF-IDF algorithm

  • Multinomial and Gaussian Naive Bayes, Bayesian Belief networks and Path models.

  • Intro to Time series
  • Autocorrelation and ACF/PACF plots
  • The Random Walk model and Stationarity of Time Series
  • Tests for Stationarity - ADF and Dickey- Fuller test, AR, MA, ARIMA, SARIMA models
  • A regression approach to time series forecasting.
  • Feature engineering & selection techniques
  • Principal Component Analysis
  • Linear Discriminant Analysis
  • Serving the model via Rest API & Keras.
Edit
  • Introduction to Neural Networks
  • Layered Neural networks
  • Activation Functions and their application
  • Backpropagation and Gradient Descent

  • Introduction to TensorFlow

  • Working with TensorFlow

  • Linear regression with TensorFlow

  • Logistic regression with TensorFlow

  • Designing a deep neural network
  • Optimal choice of Loss Function
  • Tools for deep learning models - Tflearn and Pytorch
  • The problem of Exploding and Vanishing gradients

  • Architecture and desig n of a Convolutional network

  • Deep convolutional models & image augmentation.

  • RN N & LSTM structure, Bidirectional RNNs and Applications on Sequential data

  • Advanced Time series forecasting using RNNs with LSTMs

  • LSTMs vs GRUs.

  • Intro to RBMs, Autoencoders

  • Application of RBMs in Collaborative filtering

  • Autoencoders for Anomaly detection

  • Capstone Project -Self-driving cars, Facial recognization.

Edit
  • What is RL? – High-level overview
  • The multi-armed bandit problem and the explore-exploit dilemma
  • Markov Decision Processes (MDPs)
  • Dynamic Programming
  • Monte Carlo Control
  • Temporal Difference (TD) Learning (Q-Learning and SARSA)
  • Approximation Methods (i.e., how to plug in a deep neural network or other differentiable model into your RL algorithm)
Edit
  • What is RL? – High-level overview
  • The multi-armed bandit problem and the explore-exploit dilemma
  • Markov Decision Processes (MDPs)
  • Dynamic Programming
  • Monte Carlo Control
  • Temporal Difference (TD) Learning (Q-Learning and SARSA)
  • Approximation Methods (i.e., how to plug in a deep neural network or other differentiable model into your RL algorithm)
Edit
  • Mathematics for Computer
  • Vision Intro to Transfer Learning
  • R-CNN and RetinaNet models for Object detection using Tensorflow
  • FCN architecture for Image segmentation
  • IoU and Dice score for model evaluation
  • Face detection with OpenCV
Edit
  • Ethical Risk Analysis – Identification and Mitigation
  • Managing Privacy risks
  • Modeling personas with minimal private data sharing
  • Homomorphic encryption and Zero-Knowledge protocols
  • Managing Accountability risks with a Responsibility Assignment Matrix
  • Managing Transparency and Explainability risks
Edit
  • Introduction to Excel interface.
  • Customizing Excel Quick Access Toolbar.
  • Structure of Excel Workbook.
  • Excel Menus.
  • Excel Toolbars: Hiding, Displaying, and Moving Toolbars.
  • Switching Between Sheets in a Workbook.
  • Inserting and Deleting Worksheets.
  • Renaming and Moving Worksheets.
  • Protecting a Workbook.
  • Hiding and Unhiding Columns, Rows and Sheets.
  • Splitting and Freezing a Window.
  • Inserting Page Breaks.
  • Advanced Printing Options.
  • Opening, saving and closing Excel document.
  • Common Excel Shortcut Keys.
  • Quiz.
  • Adjusting Page Margins and Orientation.
  • Creating Headers, Footers, and Page Numbers.
  • Adding Print Titles and Gridlines.
  • Formatting Fonts & Values.
  • Adjusting Row Height and Column Width.
  • Changing Cell Alignment.
  • Adding Borders.
  • Applying Colours and Patterns.
  • Using the Format Painter.
  • Formatting Data as Currency Values.
  • Formatting Percentages.
  • Merging Cells, Rotating Text.
  • Using Auto Fill.
  • Moving and Copying Data in an Excel Worksheet.
  • Inserting and Deleting Rows and Columns.
  • Inserting Excel Shapes.
  • Formatting Excel Shapes.
  • Inserting Images.
  • Working with Excel SmartArt.

  • Entering and selecting values. Using numeric data in excel

  •  

    Working with forms menu, cell references, conditional

  • formatting and data validation, Finding and replacing information from worksheet

  • Inserting & deleting cells, rows and columns.

  • Creating basic formulae in excel

  • Im plementing excel formulae in worksheet

  • Relative cell referencing

  • Absol ute cell referencing

  • Relative vs Absol ute cell references in formulae

  • Understanding the order of operation

  • Entering and Editing text, Fixing errors in your formulae

  • Formulae with several operators, Formulae with cell ranges

  • Quiz.

  • Working with functions like SUM(), AVERAGE() etc

  • Adjacent cells error in excel calculations

  • Use of AutoSum & autofill command

  • Quiz

  • Creating a column chart.
  • Working with the excel chart ribbon.
  • Adding and modifying data on an Excel chart.
  • Formatting an excel chart.
  • Moving a chart to another worksheet.
  • Resizing a chart.
  • Changing a chart’s source data.
  • Adding titles, gridlines and a data table.
  • Formatting a data series and chart axis.
  • Using fill effects.
  • Changing a chart type and working with pie charts.
  • Quiz.
  • Intro to Pivot Tables
  • Structuring Source Data for Analysis in Excel
  • Creating a PivotTa ble
  • Exploring Pivot Ta ble Analyse & Desig n Options
  • Working with and on pivot tables
  • Dealing with Growing Source Data
  • Enriching data with Pivot table calculated values & fields
  • Formatting and charting a PivotTable
  • Pivot Table Case Study
  • Quiz
  • Intro to Pivot Tables
  • Structuring Source Data for Analysis in Excel
  • Creating a PivotTa ble
  • Exploring Pivot Ta ble Analyse & Desig n Options
  • Working with and on pivot tables
  • Dealing with Growing Source Data
  • Enriching data with Pivot table calculated values & fields
  • Formatting and charting a PivotTable
  • Pivot Table Case Study
  • Quiz

  • Introduction to macros

  • Automating Tasks with Macros

  • Recording a Macro

  • Playing a Macro

  • Assigning a Macro a Shortcut Key.

  • What is a Database?
  • Why SQL?
  • All about SQL Difference between SQL & MongoDB.
  • Different Structured Query languages Why MySQL?
  • Installation of MySQL.
  • DDL.
  • SQL Keywords.
  • DCL.
  • TCL.
  • Database Vs Excel Sheets.
  • Relational and database schema.
  • Foreign and Primary Keys.
  • Database manipulation, management, and administration.
  • Topics - What is HBase?
  • HBase Architecture.
  • HBase Components.
  • Storage Model of HBase.
  • HBase vs RDBMS.
  • Introduction to Mongo DB, CRUD.
  • Advantages of MongoDB over RDBMS.
  • Use cases.
  • First Step in SQL Database.
  • Creating Database.
  • Dropping Database.
  • Using Database.
  • Introduction to Tables.
  • Data types in SQL.
  • Creating a table.
  • Dropping table.
  • Coding best practices in SQL.

  • Introduction to database

  • Creating Data base, Dropping Database

  • Using Database

  • Introduction to Tables

  • Data types in SQL

  • Use case of different data

  • Working with tables

  • Coding best practices in SQL

  • SELECT Statement.
  • COUNT.
  • SELECT WHERE.
  • ORDER BY.
  • IN, NOT IN.
  • NULL and NOT_NULL.
  • Comparison Operators (=, >, >=, <=).
  • MySQL Warnings (Understand and Debug).
  • SELECT DISTINCT.
  • LIKE, NOT LIKE, ILIKE.
  • LIMIT.
  • BETWEEN.
  • BETWEEN – AND
  • Multiple INSERT.
  • INSERT INTO.
  • GROUP BY.
  • HAVING.
  • WHERE vs HAVING.
  • UPDATE.
  • DELETE.
  • AS.
  • EXISTS-NOT EXISTS.
  • Aggregator functions.
  • Application of group by.
  • Count function.
  • MIN and MAX.
  • Sum Function.
  • Avg Function.

  • Introduction to JOINs

  • Types of JOINS

  • Usage of different types of JOINS

  • Loading Data

  • Usage of string functions like; CONCAT, SUBSTRING etc

  • INNER join,

    OUTER join, Full join, Left Join, Right Join, UNION.

  • Local, Session, Global Variables

  • Timestamps and Extract, CURRENT DATE & TIME, EXTRACT

  •  

    AGE, TO_CHAR, Mathematical Functions and Operators

  • CEIL & FLOOR, POWER, RANDOM,

     

  • ROUND, SETSEED, Operators and their precedence.

  • Data bases

  • Collection & Documents

  • Shell & MongoDB drivers

  • What is JSON Data

  • Create, Read, Update, Delete

  • Working with Arrays

  • Understanding Schemas and Relations.

  • What is MongoDB?
  • Characteristics, Structure and Features.
  • MongoDB Ecosystem.
  • Installation process.
  • Connecting to MongoDB database.
  • What are Object Ids in MongoDb.
  • Data Formats in MongoDB.
  • MongoDB Aggregation Framework.
  • Aggregating Documents.
  • What are MongoDB Drivers?
  • Finding, Deleting, Updating, Inserting Elements.
  • What is TABLEAU?
  • Why to use TABLEAU?
  • Installation of TABLEAU.
  • Connecting to data source.
  • Navigating Tableau.
  • Creating Calculated Fields.
  • Adding Colours.
  • Adding Labels and Formatting.
  • Exporting Your Worksheet.
  • Creating dashboard pages.
  • Different charts on TABLEAU (Bar graphs, Line graphs, Scatter graphs, Crosstabs, Histogram, Heatmap, Tree maps, Bullet graphs, etc.)
  • Dashboard Tricks.
  • Hands on exercises.
  • Pre-attentive processing.
  • Length and position.
  • Reference Lines.
  • Parameters.
  • Tooltips.
  • Data over time.
  • Implementation.
  • Advance table calculations.
  • Creating multiple joins in Tableau.
  • Relationships vs Joins.
  • Calculated Fields vs Table calculations.
  • Creating advanced table calculations.
  • Saving a Quick table calculation.
  • Writing your own Table calculations.
  • Adding a second layer moving average.
  • Trendlines for power-insights.
  • Getting started with visual analytics.
  • Geospatial data.
  • Mapping workspace.
  • Map layers.
  • Custom territories.
  • Common mapping issues.
  • Creating a map, working with hierarchies.
  • Coordinate points.
  • Plotting latitude and longitude.
  • Custom geocoding.
  •         Polygon Maps.
  • WMS and Background.
  • Image Creating a Scatter Plot, Applying Filters to Multiple Worksheets.
  • Aggregation and its types
  • level of detail common calculation functions
  • creating parameters

  • Tiled vs Floating

  • Working in views with Dashboard and stories

  • Legends, Quick filters.

     

  • Why Power BI?
  • Account Types.
  • Installing Power BI.
  • Understanding the Power BI Desktop Workflow.
  • Exploring the Interface of the Data Model.
  • Understanding the Query Editor Interface.
  • Connecting Power BI Desktop to Source Files.
  • Keeping & Removing Rows.
  • Removing Empty Rows.
  • Create calculate columns.
  • Make first row as headers.
  • Change Data type.
  •         Rearrange the columns.
  • Remove duplicates.
  • Unpivot columns and split columns.
  • Working with filters.
  • Appending queries.
  • Working with columns.
  • Replacing values.
  • Splitting columns.
  • Formatting data & handling formatting errors.
  • Pivoting & unpivoting data.
  • Query duplicates vs references

  • Power BI

  • Working with Time series Understanding aggregation and granularity

  • Filters and Slicers in Power BI

  • Maps, Scatterplots and BI Reports

  • Creating a Customer Seq mentation.

  • Understanding Relationships.
  • Many-to-One & One-to-One.
  • Cross Filter Direction & Many-to-Many.
  • M-Language vs DAX (Data Analysis Expressions).
  • Basics of DAX.
  • DAX Data Types.
  • DAX Operators and Syntax.
  • Importing Data for DAX Learning.
  • Resources for DAX Learning.
  • M vs DAX.
  • Understanding IF & RELATED.
  • Create a Column.
  • Rules to Create Measures.
  • Calculated Columns vs Calculated Measures.
  • Understanding CALCULATE & FILTER.
  • Understanding "Data Category".
  • SUM, AVERAGE, MIN, MAX, SUMX, COUNT, DIVIDE, COUNT, COUNTROOMS, CALCULATE, FILTER, ALL
  • Time Intelligence.
  • Create date table in M.
  • Create date table in DAX.
  • Display last refresh date.
  • SAMEPERIODLASTYEAR.
  • TOTALYTD.
  • DATEADD.
  • PREVIOUSMONTH.

  • Create data table in M and DAX, Display last refresh Date.

  • Create your first report.
  • Modelling basics to advance.
  • Modelling and relationship.
  • Ways of creating relationship.
  • Normalisation – De-normalisation.
  • OLTP vs OLAP.
  • Star schema vs Snowflake schema.

Tools Covered

Industry - partnered capstone projects

Hands-on Projects

Data sets from the industry

15+
Projects
Available
6+
Domain
Covered

Practice with 20+ tools

Designed by Industry Experts

Get Real-world Experience

Edit
Predict-card-default-skillslash-project

Predict credit default application

Project Objective : Develop a prediction model for existing customers to identify probable credit default for a retail bank
Predict-backrupt-of the company skillslash project

Predict bankruptcy of a company

Project Objective: Model to predict whether a company will go bankrupt or not
foreign currencey preidction skillslash project

Currency foreign exchange rates

Project Objective: Time series analysis Forecast value of a currency in global market
Mobile banking project by skillslash

Analyse customer mobile banking

Project Objective: Create clusters of customers on the usage of mobile banking
Predict-card-default-skillslash-project
Predict credit default application

Project Objective : Develop a prediction model for existing customers to identify probable credit default for a retail bank

Predict-backrupt-of the company skillslash project
Predict bankruptcy of a company

Project Objective: Model to predict whether a company will go bankrupt or not

foreign currencey preidction skillslash project
Currency foreign exchange rates

Project Objective: Time series analysis Forecast value of a currency in global market

Mobile banking project by skillslash
Analyse customer mobile banking

Project Objective: Create clusters of customers on the usage of mobile banking

    Edit
    Uber fair prediction skillslash project

    Uber - fare prediction

    Project Objective: Predicting fare price based on demand and supply, weather and other factors
    Mercedes-reduce0-time to market project skillslash

    Reduce time to market

    Project Objective: Reduce the time for a Mercedes-Benz to reach the market by optimizing the testing
    motion prediction project by skillslash

    Motion Prediction For Vehicles

    Project Objective: Build motion prediction models for self driving vehicles

    Predictive maintenance

    Project Objective: The objective is to predict the failure of the machine in advance
    Uber fair prediction skillslash project
    Uber - fare prediction

    Project Objective: Have to analyze the data Identification of COVID-19 surge in cases based on mobility within the country

    Mercedes-reduce0-time to market project skillslash
    Reduce time to market

    Project Objective: Reduce the time for a Mercedes-Benz to reach the market by optimizing the testing

    motion prediction project by skillslash
    Motion Prediction For Vehicles

    Project Objective: Build motion prediction models for self driving vehicles

    Predictive maintenance

    Project Objective: The objective is to predict the failure of the machine in advance

      Edit
      Covid-19 data prediction project

      Google Mobility data Prediction

      Project Objective: Goal is to identify Covid-19 surge in different regions based on mobility within the country
      skillslash-project-ai-heart

      Predict heart failure

      Project Objective: Create a model that could predict heart failure before its occurrence
      skillslash-project-newtworking

      Reddit - Vaccine myths on social media

      Project Objective: Sentiment analysis of vaccine on social media

      Infected or not Infected

      Project Objective: Study the human cell to identify whether it is infected or not infected
      Covid-19 data prediction project
      Google Mobility data Prediction

      Project Objective: Goal is to identify Covid-19 surge in different regions based on mobility within the country

      skillslash-project-newtworking
      Reddit - Vaccine myths on social media

      Project Objective: Sentiment analysis of vaccine on social media

      skillslash-project-ai-heart
      Predict heart failure

      Project Objective: Create a model that could predict heart failure before its occurrence

      Infected or not Infected

      Project Objective: Study the human cell to identify whether it is infected or not infected

        Edit
        Youtube-data prediction project by skillsash

        YouTube trending video analytics

        Project Objective: Analyze daily records of YouTube trending video analytics to generate their comments
        Google Play Store Apps success factors.project by skillslash

        Google Playstore apps success factors

        Project Objective: Predict the factors that contribute to the success of an application on Google play store
        Spotify-song-preidction-project-skillslash

        Spotify – Identify the songs

        Project Objective: Work on the dataset to find a geographical connection with popular songs
        Hr-analytics-project-skillslash

        HR Analytics: To find candidates

        Project Objective: Predict the probability of a candidate looking for a new job
        Youtube-data prediction project by skillsash
        YouTube trending video analytics

        Project Objective: Analyze daily records of YouTube trending video analytics to generate their comments

        Google Play Store Apps success factors.project by skillslash
        Google Playstore apps success factors

        Project Objective: Predict the factors that contribute to the success of an application on Google play store

        Spotify-song-preidction-project-skillslash
        Spotify – Identify the songs related to

        Project Objective: Work on the dataset to find a geographical connection with popular songs

        Hr-analytics-project-skillslash
        HR Analytics: To find candidates

        Project Objective: Predict the probability of a candidate looking for a new job

          Edit
          tele-customer-skillslash-project

          Telecom customer churn prediction

          Project Objective: Predict the behavior of customers to identify the probability of churning off
          Fraud call prediction skillslash project

          Fraud call Prediction model

          Project Objective: Identification of illegal activities like fake profiles, cloning, identity theft for the customers
          telecome price optimization

          Price Optimization by analysing customers

          Project Objective: Price optimization for the telecom services by predicting the LTV, Tarrifs, understanding price elasticity w.r.t factors
          Chatbot developemnt project

          Chatbot design for best in-class support

          Project Objective: Chatbots for operational support and automated self-service
          tele-customer-skillslash-project
          Telecom customer churn prediction

          Project Objective: Predict the behavior of customers to identify the probability of churning off

          Fraud call prediction skillslash project
          Fraud call prediction model

          Project Objective: Identification of illegal activities like fake profiles, cloning, identity theft for the customers

          telecome price optimization
          Price Optimization by analysing customers

          Project Objective: Price optimization for the telecom services by predicting the LTV, Tarrifs, understanding price elasticity w.r.t factors

          Chatbot developemnt project
          Chatbot design for best in-class support

          Project Objective: Chatbots for operational support and automated self-service

            Edit
            IMDB movie rating prediction

            IMDB – predict the rating of a movie

            Project Objective: Predict the rating and success of movies
            NSE stock prediction skilllsash project

            NSE – Stock price prediction

            Project Objective : Predict the stock prices with an increased level of accuracy
            Vinbigdata project by skillslash

            VinBigData Chest X-ray

            Project Objective: Accurate classification of problems to identify and localize findings on chest radiographs.

            Amazon Food Reviews

            Project Objective: Classify food reviewsbased on customer feedback. Here you will use NLP to identify the sentiment of customers.
            IMDB movie rating prediction
            IMDB – predict the rating of a movie

            Project Objective: Predict the rating and success of movies

            NSE stock prediction skilllsash project
            NSE – Stock price prediction

            Project Objective : Predict the stock prices with an increased level of accuracy

            Vinbigdata project by skillslash
            VinBigData Chest X-ray

            Project Objective: Accurate classification of problems to identify and localize findings on chest radiographs

            Amazon Food Review

            Project Objective: Classify food reviews based on customer feedback. Here you will use NLP to identify the sentiment of customers.

              Student reviews

              Why get enrolled in our Data Science training?

              Skillslash offers Best Data Science course in Bangalore with placement guarantee. Besides, our Data Science courses are designed for creative minds and also made for everyone. So, take our Artificial intelligence and Data science course and experience the new era of education!

              Student reviews

              Why get enrolled in our Data Science training?

              Skillslash offers Best Data Science course in Bangalore with placement guarantee. Besides, our Data Science courses are designed for creative minds and also made for everyone. So, take our Artificial intelligence and Data science course and experience the new era of education!

              Thanks a ton Skillslash Family. As a part of the Full stack course in Data Science i had a great learning experience. I was able to successfully move into a data science role in 7 months , which was amazing.

              Pragyan Prakash

              Data science full stack program is too good and helpful for working professionals, I have done BCA so I was well versed in Java, C, basic SQL and C++. At Skillslash I learnt Python, core SQL, R, math - stats, ML and More.

              Tilak Rao

              One of the best platform for working professionals. Although a new startup but training quality is really good. For our batch, instructor is Rahul (and co-founder) and he teaches statistics and ML concepts in depth.

              Gautam

              One of the best course providers is Skillslash, their data science course has helped me become the data scientist I am today. There are tons of differences between studying data science and working as a data scientist.

              Mrinal Sahay

              Skillslash is truly one of the best institutes to study machine learning, I thank my brother for suggesting me this course. The course has amazing perks for working professionals like live classes, faculty of industry professionals

              Sammer Ahmed

                The benefits of Skillslash

                Talk with our expert
                Edit
                Certification from top AI startups by skillslash
                • We assist our students to work on real-time Data Science projects.  Further, under our industrial Data Science training, you get to work with top AI firms. 
                • Skillslash’s Data Science course with placement in Bangalore offers an advanced project experience certification. Also, this can add relevant value to our student’s profile.
                • Our students work on real projects under expert-level AI and Data science training. They do so by working from data cleaning to deployment of the project.
                • This can be used by team decision makers to complete a POC. Besides, it is also possible to gain better resources for resolving project issues.
                Edit
                Build Your own course feature by skillslash
                • Under our Data Science course, you get to interact with our experts.  And also, construct customized learning routes. Especially, based on your work goals and previous expertise.
                • The Data Science training in Bangalore comes with real work experience and a 100% job guarantee. Also, it is designed giving an emphasis to industry training.
                • In our Artificial Intelligence and Data Science Syllabus, modules can be selected based on your preferred learning style.
                • Choose your correct learning path to become a data science expert with Bangalore’s best Data Science course.
                Edit
                Bring your own project and get expert guidance by skillslash
                • Work on and learn with our real time projects. Also, this helps making you an expert data science professional.
                • Skillslash’s Data Science courses in Bangalore are built to provide you with advanced experience with projects.
                • Under this professional Data Science training, students are allowed to bring their own projects. And also, learn data science with their most relevant domain experience.
                • The domain relevant data science project experience provides the right boost to the student’s career.
                Edit
                Certification from top AI startups by skillslash
                • We assist our students to work on real-time Data Science projects.  Further, under our industrial Data Science training, you get to work with top AI firms. 
                • Skillslash’s Data Science course with placement in Bangalore offers an advanced project experience certification. Also, this can add relevant value to our student’s profile.
                • Our students work on real projects under expert-level AI and Data science training. They do so by working from data cleaning to deployment of the project.
                • This can be used by team decision makers to complete a POC. Besides, it is also possible to gain better resources for resolving project issues.
                Edit
                Build Your own course feature by skillslash
                • Under our Data Science course, you get to interact with our experts.  And also, construct customized learning routes. Especially, based on your work goals and previous expertise.
                • The Data Science training in Bangalore comes with real work experience and a 100% job guarantee. Also, it is designed giving an emphasis to industry training.
                • In our Artificial Intelligence and Data Science Syllabus, modules can be selected based on your preferred learning style.
                • Choose your correct learning path to become a data science expert with Bangalore’s best Data Science course.
                Edit
                Bring your own project and get expert guidance by skillslash
                • Work on and learn with our real time projects. Also, this helps making you an expert data science professional.
                • Skillslash’s Data Science courses in Bangalore are built to provide you with advanced experience with projects.
                • Under this professional Data Science training, students are allowed to bring their own projects. And also, learn data science with their most relevant domain experience.
                • The domain relevant data science project experience provides the right boost to the student’s career.

                Get Hired

                Work on live projects to get hired at:

                theorax
                Tcs_logo
                Nobroker_logo
                crued
                Flipkart_logo
                wipro_logo
                juspay
                Vodafone_logo
                Nerodynamics_logo
                acenture_logo
                HSBC_logo
                capgemini_logo
                Microsoft_logo
                rupeek
                Zoho_logo
                Samsung_logo
                Myntra_logo
                HCL_logo
                Astrozeneca
                amazon_logo

                How to apply?

                Follow these 3 simple steps in the admission process

                Step 1: Fill Enquiry From

                Apply for your profile review
                by filling the form

                Step 2: Talk to Expert

                Get your career counseling report from the expert

                Step 3: Get Started

                Join the data science program by enrolling
                Fill Enquiry Form
                Apply for your profile review

                by filling the form

                Talk to Expert
                Get your career counseling report

                from the expert

                Get Started
                Join the data science program

                by enrolling

                Upcoming Cohort Deadline

                The admission closes once the required number of applicants enroll for the upcoming Data Science cohort. So, apply early to secure your seats. And, get started on your professional Data science learning journey in Bangalore.

                15th July 2022

                Apply Now

                Finance

                Program Fees & Financing

                The Data Science course fees in Bangalore start from INR 59,000 (Excluding GST). Besides, we aim to deliver to you quality education considering the aspect of feasibility..

                Course feasibility

                The Data Science course in bangalore, We have at Skillslash is designed giving focus to affordability. So, we give you several financial options to manage and budget the expenses of your Data Science course fees. Because, we believe in fair reachability and access to all our carefully curated Data Science programs. Therefore, you get options such as EMI to pay the fees of our Data Science and Artificial Intelligence courses in Bangalore.

                Program Features

                Job Assistance

                Live Class Subscription

                LMS Subscription

                Job Referrals

                Industry Projects

                Capstone Projects

                Domain Training

                Project Certification from Companies

                Job Guarantee

                Basic

                ₹ 59,000 + GST

                Data Science Training

                1 Year

                Lifetime

                3+

                7+

                1

                Enroll Now

                Pro

                ₹ 89,000 + GST

                Real Work Experience

                3 Years

                Lifetime

                5+

                15+

                3

                Enroll Now

                Pro Max

                ₹ 1,30,000 + GST

                100% Job Guarantee

                3 Years

                Lifetime

                Unlimited

                15+

                3

                Enroll Now

                Basic

                ₹59,000 +GST

                1 Year

                Lifetime

                3+

                7+

                1

                Pro

                Price

                ₹89,000 +GST

                Job Assitance

                Live Class Subscription

                3 Year

                LMS Subscription

                Lifetime

                Job Referrals

                5+

                Industry Projects

                15+

                Capstone Projects

                3

                Domain Training

                Project Certification from companies

                Job Guarantee

                Pro Max

                ₹1,30,000 +GST

                3 Year

                Lifetime

                Unlimited

                15+

                3

                Talk to Expert

                Batch Details

                Program Cohorts

                Data science course in Bangalore Next 2022 Cohort

                next first batch details skillslash
                Weekday Batch
                📅 25 Feb, 2022
                🕒 08:00 - 10:00 PM
                next second batch details skillslash
                weekend Batch
                📅 16 Jan, 2022
                🕒 09:00 - 12:00 AM
                Enroll Now

                Data science course in Bangalore Next 2022 Cohort

                Date

                Time

                Batch type

                next first batch details skillslash

                Weekday batch

                next second batch details skillslash

                Weekend batch

                15 July 2022

                26 June 2022

                08:00 – 10:00 PM

                09:00 – 12:00 AM