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Data science and AI course with internship starting from day 1

Enroll in our 6 months Data science and AI course with internship that is specifically curated for college students. Customize your courses and study from experts with the best data science course in India with placements.









Program features of Data science course

Course access

With our Data science and AI course with internship, you can get a 3 year course subscription.

Design your own courses

Self-design your learning modules under expert supervision to meet your learning requirements. 

Job assistance

Avail free career counseling, training for resume writing and mock interview trials.

Project certification

Bring your own projects and get certification. Besides, work in real-time projects in AI and ML. 

Get shareable internship certification

Data science and AI course with internship starting from day 1

Enroll in our 6 months Data science and AI course with internship that is specifically curated for college students. Customize your courses and study from experts with the best data science course in India with placements.











Program features of Data science and AI course

Program Duration

6 months of faculty led live online classes by industry expert. 250+ hrs sessions with 15+ live industry projects.

Job assistance

Most importantly, you can get free career counseling. Besides, you will get training for resume writing. Also, get practice by participating in mock interview trials. 

Design your own courses

Self-design your learning modules to meet your learning requirements. Besides, get expert guidance to curate and learn the artificial intelligence and data science course.  

Course Fees

INR. 50,000/- (+ 18% GST)
5 months NO-COST EMI on all major credit cards | *Interest free loan option

Course access

Once you sign up for our Data science and AI course with internship, you can get a 3 year course subscription. So, you can come back and revise the lessons.

Project support and certification

Bring your own projects and get certification. Work in real-time projects in AI and ML. In addition, final year college students can avail project assistance. 

Contact us to clear your queries

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    Submit this information and our expert counsellor will call you on shortly

    Work in industrial projects and get experience

    Project certification under our Data science and AI course

    Besides, by enrolling in this artificial intelligence and data science course, get the chance to work in real-time AI and ML projects. In addition, get the chance to avail direct company certifications with the best data science course in India. Furthermore, final year graduating students can get project assistance to successfully complete the projects.

    Get Relevant Internship Experience

    Work on Collaborative projects with companies and get Internship experience certificate.

    Get hired by top-tier MNC’s

    Equip yourself to get placed in MNC’s with portfolio building and internship certification.

    Pay less but reap more

    Enroll in our Data science and AI course and take off your career

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    Course curriculum of Data science and AI course

    Milestone 0

    Enhance your programming abilities

    Learn how to program from the ground up. Furthermore, regardless of the IT/Non-IT domains, applicants from both the tech and non-tech domains can profit from this.

    Skill up in the fundamentals of mathematics

    Study how to use statistics, mathematics, and probability to solve problems. Furthermore, this aids in the comprehension of data science and artificial intelligence.

    Learn about domain and cloud computing

    First, the cloud will be discussed, followed by domain training. In reality, the latter will cover a wide range of themes, including manufacturing and healthcare.

    artificial intelligence course in bangalore Curriculum
    Milestone 1

    Study python in depth

    Python is a programming language that can be learned from the ground up. Acquire practical python and analytics skills. For instance, Pandas, Numpy, Seaborn, and Matplotlib.

    Acquire skills in EDA, statistics, and storytelling

    Gain experience with exploratory data analysis by working on a variety of projects (EDA). Besides, acquire a thorough understanding of statistics and probability as well.

    Projects and case studies

    Take part in a range of case studies including python and data analytics. Also, participate in at least one capstone project to gain experience.

    Skillslash’s collaborative and live interactive artificial intelligence training in Hyderabad
    Milestone 2

    Get insights of machine learning.

    Learn everything there is to know about machine learning. Especially machine learning algorithms, supervised and unsupervised learning.

    Develop your time series and modelling skills

    Study more about Python time series forecasting. Besides, get hands-on experience with advanced modelling techniques like model modification and feature engineering.

    Capstone project

    You will be able to participate in capstone projects. In addition, you will also have a deeper grasp of cloud computing and receive domain training.

    Ai and ML course in bangalore Curriculum
    Milestone 3

    Learn more about deep learning and computer vision

    Advanced computer vision training is integrated with deep learning techniques. Also, get your hands on a capstone project as well as many case studies.

    Investigate NLP and Auto AI

    Know more about natural language processing and advanced text analytics (NLP). Also, start working on a chatbot project using the Python NLTK package right now.

    Reinforcement learning and model deployment

    Study how to utilize Google Cloud Platform and Amazon Web Services to learn about reinforcement learning and model deployment.

    Milestone 4

    Resume preparation

    You will receive excellent career training by signing up in the artificial intelligence and data science course. Also, learn how to optimise your resumes so that you can be hired first.

    Interview tips and practise tests

    We make certain that you are immediately prepared to answer any interview questions. In addition, you will be given mock tests to take and will have the opportunity to practise answering difficult interview questions.

    Referrals for jobs

    To acquire employment references, sign up for our Data science and AI course with job guarantee. As a result, it will be easier to find work with top startups and multinational corporations.

    Resume Preparation and Inteview Guidance for artificial intelligence course in bangalore

    Reasons why go for Skillslash’s data science course with job guarantee

    Specific program features of Skillslash’s Data science and AI course

    Internship certification

    Internship experience is relevant and is a key aspect in securing a job. Besides, here we have collaboration with leading AI startups, with an emphasis on application-based learning.

    Land in top-tier jobs

    Mentorship from professionals for a smooth job transfer, outcome-driven learning tracks, and a data structures elective track for product-based organizations.

    Domain training

    Domain training from experts in the field, elective tracks for functional and industrial specialties. In addition, project experience rounds out the package.

    Project assistance

    Those students who are required to conduct industry work as part of their curriculum in the final semester will be able to do it through us.

    Reviews of students on our data science online course

    Feedbacks from student community on
    Data science and AI course with internship

    The key takeaways from Skillslash’s Data science and AI course

    Artificial intelligence work with startups
    1. For novices to the data science field, internships with industrial training is offered.
    2. Obtain credentials from top-tier companies to boost your profile.
    3. You can make interesting portfolios with internship certifications. Also, put your problem-solving abilities to test in a variety of real-life scenarios.
    4. You can select a project that corresponds to your domain preferences. You may also receive interview preferences if you have shareable certificates.
    A girl student in front of a laptop getting artificial intelligence training online
    1. You can create customised learning paths depending on your career goals and previous experience using the platform.
    2. Specialized courses with a concentration on industry training are available. Make use of programmes that are taught by a professional educator.
    3. The Data science and AI course covers a wide range of topics and lets you choose your own learning path.
    4. Modules can be chosen according to your personal learning method. In addition, choose chapters that are relevant to your field.
    Work on Live projects in bangalore's Best artificial intelligence course
    1. Students enrolling in our Data science and AI course can work on their own projects with our help and achieve outstanding outcomes.
    2. It is a type of project certification that is a little more complex. Furthermore, candidates with relevant experience are being sought.
    3. In a comfortable setting, you will be able to participate in live project training.
    4. This can be used to finish a POC by team decision makers.  In addition, greater resources for resolving project challenges may be obtained.

    Course details of Data science and AI course

    Chapter 1: Introduction to Programming ( 3 hrs )
    • What is a programming language ?
    • Source code Vs bytecode Vs machine code

    Also, learn about –

    • Compiler Vs Interpreter
    • C/C++, Java Vs Python
    Chapter 2: Jupyter notebook basics (1 hrs)
    • Different type of code editors in python
    • Introduction to Anaconda and jupyter notebook
    Chapter 3: Python Programming Basics (2 hrs )
    • Variable Vs identifiers
    • Strings Operators Vs operand

    And also,

    • Procedure oriented Vs modular programming
    Chapter 4: Statistics basics (4 hrs)
    • Introduction to statistics
    • Measures of Central Tendency

    Further, it covers –

    • Measures of dispersion
    • Inferential statistics and Sampling theory
    Chapter 5: Introduction to probability (4 hrs)
    • Introduction to probability
    • permutations and combinations

    Also, including –

    • Addition and Multiplication Rule
    • Conditional Probabilities
    Introduction to Programming
    • What is Programming Language?
    • Why do we need a Programming Language?
    • Different types of Programming Language

    In addition, it includes –

    • Why do we prefer to code in a High-Level Programming
    • What is Compiler? What is an Interpreter?

    Also, it covers –

    • Compile Time vs Run Time
    • Compile Time Error vs Run Time Error.
    Introduction to Python
    • What is Python?
    • Why do we need Python?
    • Python is Compiled or Interpreted?

    Further, it includes –

    • How a Python Program runs on our system?
    • Features of Python
    • Memory Management in Python
    • Different Implementations of Python
    Conditional and Loops
    • Conditional Statement

    Use of “if”, “if-else”, “if-elif-else” statement

    • Loop Statement

    For loop

    When to use for loop

    For-else loop

    While Loop

    Also, covering –

    When to use while loop

    While-else loop

    Infinite loop

    Break, Continue and Pass

    Python Programming Components
    • Writing your First Python Program
    • Linting Python code
    • Formatting Python code

    Also, it covers –

    • Understanding
    • Few important function

    Besides, it contains –

    • Command Line Arguments
    • Python Operators
    Data Types in Python
      • Fundamental Data Types
          • Strings
          • Numbers
          • None Type
          • Boolean Type

    Also, covering –

    • Derived Data Structure
    • Introduction to List

    Further, the module has –

    • List creation and importance of eval() while taking list as input
      from the user
    • List comprehension
    • List properties and some basic operations


    • What is a function?
    • Function as a first-class citizen
    • What is the use of function? What is the DRY Principle?
    • How to define a function.

    Also, the module covers –

    • Function call vs Function referencing
    • What are inputs and outputs to the function?
    • Parameters vs Arguments
    • Types of Arguments

    Further, covering –

    • Return statement
    • Recursion
    • Namespace vs Scope
    • Anonymous Function and Lambda Expression

    Besides, the module has –

    • Filter, map, sort and reduce function
    • Closure Concept
    • Iterator Concept

    Exception Handling

    • What is an Exception?
    • Why do we need Exception Handling?
    • Type of Errors
    • Exception Handling Keywords


    • Nested try-except block
    • Default except for block
    • Try with multiple except block
    Modules in Python
          • What is Module?
          • Introduction to Modular Programming
          • Module Search path
          • Importing Modules and different import statement

    Besides, it has –

          • Types of Modules
            • Builtin Modules
            • User Defined Modules
            • Package

    File Handling

    • What is File Handling?
    • Why do we need File Handling?

    And, also covering –

    • Type of Files
    • File Operation

    Regular Expression

    • What is Regular Expression?
    • Why do we need Regular Expression?
    • Importing regex module

    Also, it contains –

    • What is Raw string and why do we need it?
    • Sample regex pattern and it’s interpretation
    • Important Methods

    Numpy in Python

    • What is Numpy?
    • Why is Numpy required?
    • What is an array?
    • Why do we need an array when we have a list?
    Array Operations
      • Creating array using numpy
      • Printing an array
      • Indexing and Slicing
      • Basic operations
      • Universal Functions

    In addition, the module has –

      • Arrays with Structured Data
      • Changing shape of an array
      • Array Broadcasting
      • Vectorization
      • Iterating over an array
      • Splitting an array
      • View vs Copy

    And, also –

      • Vector Stacking
      • Miscellaneous Functions and Methods
      • Numpy and Scipy
      • Numpy and Pandas

    Visualization of Data in Python

    • Matplotlib
      • Lines and Markers
      • Figures and Axes
      • Figures and Subplots
      • Watermark
      • Shapes
      • Polygon and Arrows

    Further, the module has –

      • Beizer Curves
      • Curves
      • Annotations
      • Scales
      • Twin Axis

    And, also –

      • Boxplot and Violin Plots
      • Visualize Titanic Dataset with Box and Violin plots
      • Pie Charts
      • Stacked Plots
      • Color Maps
      • Autocorrelation

    Pandas in Python

    • What is Pandas?
    • Why do we need Pandas?
    • Numpy vs Pandas
    • Pandas Data Structure

    Also, covering –

    • Series
      • Creating Series
      • Indexing and Slicing operation
    Data Frame
      • Creating Data Frame
      • Indexing and Selection in Data Frame
      • Addition and Deletion of rows and Columns
      • Iterating over DataFrame

    Also, it covers –

      • Reshaping Data Frame
      • Handling Missing Data in data Frame
      • Grouping Data Frame
      • Sorting Data Frame
      • Stacking and Unstacking

    Besides, it has –

      • Concatenating and Merging Data Frame
      • Pandas Time Series
      • Exporting Dataframe to CSV and Excel
      • EDA using Pandas

    Part 1 – Introduction to Probability theory and Statistical

    • Introduction to Probability Principles
    • Random Variables and Probability principles
    • Discrete Probability Distributions – Binomial , Poisson etc

    Also, it covers –

    • Continuous Probability Distributions – Gaussian, Normal, etc
    • Joint and Conditional Probabilities
    • Bayes theorem and its applications
    • Central Limit Theorem and Applications

    Part 2 – Statistics and Foundation

    • Elements of Descriptive Statistics
    • Measures of Central tendency and Dispersion

    Further, the module has –

    • Inferential Statistics fundamentals
    • Sampling theory and Scales of Measurement
    • Covariance and correlation

    Part 3 – Hypothesis Testing and its Applications

    • 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

    Also, it covers –

    • Confidence Intervals , Type 1 and 2 errors
    • Chi-squared and F tests
    • Industry Applications – Two sample mean , A/B testing
    Part 4 - Exploratory Data Analysis and the Art of Storytelling
    • Ingest data
    • Data cleaning
    • Outlier detection and treatment
    • Missing value imputation

    In addition, it has –

    • Impact of Data Visualization
    • Univariate Analysis
    • Bivariate Analysis and ANOVA

    Further, it contains –

    • The science of Storytelling
    • Sliding like a management consultant

    Project – Exploratory analysis on Credit card data

    • Capstone Project for Business Analysis

    Part 0 – A primer on Machine Learning

    • Types of Learning – Supervised, Unsupervised and Reinforcement
    • Statistics vs Machine Learning

    Also, it contains –

    • Types of Analysis – Descriptive, Predictive, and Prescriptive
    • Bias Variance Tradeoff – Overfitting vs Underfitting

    Part 1 – Regression – Linear Regression

    • Correlation vs Causation
    • Simple and Multiple linear regression
    • Linear regression with Polynomial features

    Moreover, it contains –

    • What is linear in Linear Regression?
    • OLS Estimation and Gradient descent
    • Model Evaluation Metrics for regression problems – MAE , RMSE, MSE,
      and MAP

    Part 2- Classification – Logistic Regression

    • Introduction to Classification problems
    • Logistic Regression for Binary Problems
    • Maximum Likelihood estimation

    In addition, it has –

    • Data imbalance and redressal methodology
    • Upsampling, Downsampling and SMOTE

    In addition, it has –

    • one vs Rest (OVR) for multinormal classification.
    • Model Evaluation Metrics for classification-confusion matrix/
    • Misclassification error, Precision, Recall, F1 score, and AUC-ROC

    And, also –

    • Choosing the best error metric for a problem
    Part 3 - Clustering - K means
    • Introduction to Unsupervised Learning
    • Hierarchical and Non-Hierarchical techniques

    And also,

    • K Means Algorithms – Partition based model for clustering
    • Model Evaluation metrics – Clustering

    Part 4 – KNN

    • Introduction to KNNs
    • KNNs as a classifier
    • Non Parametric algorithms and Lazy learning ideology

    And also,

    • Applications in Missing value imputes and Balancing datasets

    Part 5 – Advanced Regression Models

    • Introduction to regularization
    • Ridge regression
    • Lasso regression

    Part 6 – Decision tree

    • Nonlinear models for classification
    • Intro to decision trees
    • Why are they called Greedy Algorithms

    In addition. It covers –

    • Information Theory – Measures of Impurity
    • Stopping criteria for trees
    • Susceptibility to overfitting and high variance

    And also,

    • Prevention of overfitting with Pruning or Truncation

    Part 7- Ensemble techniques

    • introduction to Bagging as an Ensemble technique
    • Bootstrap Aggregation and out of bag error

    Further, covering –

    • Random Forests and its Application in Feature selection
    • Scent and Boosting

    And also,

    • How Boosting overcomes the Bias – Variance Tradeoff

    Gradient Boosting and Xgboost as regularized boosting

    Part 8 - Support Vector Machines
    • Introduction to Expectation–Maximization Algorithms
    • The kernel trick
    • Linear, Polynomial, and RBF kernels and their use cases

    Further, it has –

    • SVMs for regression and classification
    • Applications in Multiclass classification

    Part 9 – Bayesian Family Algorithms and Intro to Text

    • Naive Bayes for Text classification
    • Bag of words and TF-IDF algorithm
    • Multinomial and Gaussian Naive Bayes

    And, also –

    • Bayesian Belief networks and Path models

    Part 10 – Time-series Analysis

    • Intro to Time series and its decomposition
    • 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 for univariate time series

    And, also –

    • A regression approach to time series forecasting
    Part 11 - How to Build and Deploy a Machine Learning pipeline
    • Loading data
    • Feature engineering techniques
    • Principal Component Analysis for Dimensionality reduction

    In addition, covering –

    • Linear Discriminant Analysis
    • Feature Selection Techniques – Forward and Backward
    • elimination, RFE

    Also, it contains-

    • Model Tuning and Selection
    • Deploying a Machine Learning Model
    • Serving the model via Rest API

    Part 12 – AutoML

    • Introduction to AutoML
    • Auto learn

    Also, covering –

    • TPOT models
    • Auto Keras

    Part 1 – Neural Networks

    • introduction to Neural Networks
    • Layered Neural Network
    • Activation function and their application

    Also, it has –

    • Backpropagation and Gradient descent

    Part 2 – Tensorflow

    • Introduction to TensorFlow
    • Linear and Logistic regression with Tensorflow

    Part 3 – Artificial Neural Networks (ANNs)

    • Multi-layer perceptrons and Feedforward networks
    • Activation Functions
    • Intro to Deep Learning and deep neural networks

    Also, it has –

    • Adam and Batch normalisation

    Part 4 – Deep Neural Networks

    • Designing a deep neural network
    • Optimal choice of Loss Function
    • Tools for deep learning models – Tflearn and Pytorch

    And, also –

    • The problem of Exploding and Vanishing gradients

    Part 5 – Convolutional Neural networks

    • Architecture and design of a Convolutional network
    • Pooling and Flattening layer

    Further, it contains –

    • Basics of digital images and image augmentation
    • Deep convolutional models
    Part 6 - Recurrent Neural networks and LSTMs
    • RNN network structure
    • Bidirectional RNNs and Applications on Sequential data
    • LSTM cell structure and its variants

    Additionally, it has –

    • Applying RNN/LSTMs to language and character modelling
    • Advanced Time series forecasting using RNNs with LSTMs
    • LSTMs vs GRUs – Key takeaways

    Additionally, it has –

    Part 7 – Restricted Boltzmann Machines and Autoencoders

    • Intro to RBMs and their training
    • Application of RBMs in Collaborative filtering
    • Intro to Autoencoders

    And, also –

    • Autoencoders for Anomaly detection

    Capstone Project

    • Self driving cars
    • Facial recognition
    Part 1 – Language modeling and Sequence tagging
    • Intro to the NLTK library
    • N-gram Language models: Perplexity and Smoothing
    • Introduction to Hidden Markov models

    Further, it contains –

    • Viterbi algorithms
    • MEMMs and CRFs for named entity recognition
    • Neural Language models

    And, also –

    • Application of LSTMs to predict the next word
    Part 2 – Vector space models of Semantics for Contextual
    • Distributional semantics
    • Explicit and Implicit matrix factorization
    • Word2vec and Doc2vec models

    And also,

    • Introduction to topical modeling
    Part 3 – Sequence to Sequence tasks
    • Introduction to Machine translation
    • Word Alignment models and Encoder-Decoder Architecture

    And, also it has –

    • How to implement a conversational Chatbot
    Capstone Project
    • Fully functional Chatbot
    • What is RL ? – High-level overview
    • The multi-armed bandit problem and the explore-exploit dilemma
    • Markov Decision Processes (MDPs)

    Further, it has –

    • Dynamic Programming
    • Monte Carlo Control

    And, also it covers –

    • Temporal Difference (TD) Learning (Q-Learning and SARSA)
    • Approximation Methods (i.e. how to plug in a deep neural

    And, also it covers –

    • network or other differentiable model into your RL algorithm)
    • Mathematics for Computer Vision
    • Intro to Transfer Learning

    Besides, the module covers –

    • R-CNN and RetinaNet models for Object detection using Tensorflow
    • FCN architecture for Image segmentation

    And also,

    • IoU and Dice score for model evaluation
    • Face detection with OpenCV
    • Ethical Risk Analysis – Identification and Mitigation
    • Managing Privacy risks
    • Modeling personas with minimal private data sharing
    • Homomorphic encryption and Zero-Knowledge protocols

    Also, it has –

    • Managing Accountability risks with a Responsibility Assignment Matrix
    • Managing Transparency and Explainability risks
    Excel for Business
    Excel Fundamentals:
    • Introduction to Excel interface
    • Customizing Excel Quick Access Toolbar
    • Structure of Excel Workbook
    • Excel Menus

    Further, the module contains –

    • Excel Toolbars: Hiding, Displaying, and Moving Toolbars
    • Switching Between Sheets in a Workbook
    • Inserting and Deleting Worksheets

    Also, covering –

    • Renaming and Moving Worksheets
    • Protecting a Workbook
    • Hiding and Unhiding Columns, Rows and Sheets
    • Splitting and Freezing a Window
    • Inserting Page Breaks

    Besides, it contains –

    • Advanced Printing Options
      Opening, saving and closing Excel document
    • Common Excel Shortcut Keys
    • Quiz
    Worksheet Customization
    • Adjusting Page Margins and Orientation
    • Creating Headers, Footers, and Page Numbers
    • Adding Print Titles and Gridlines
    • Formatting Fonts & Values

    Further, the module has –

    • Adjusting Row Height and Column Width
    • Changing Cell Alignment
    • Adding Borders
    • Applying Colors and Patterns

    Besides, it contains –

    • Using the Format Painter
    • Formatting Data as Currency Values
    • Formatting Percentages

    Also, covering –

    • Merging Cells, Rotating Text
    • Using Auto Fill
    • Moving and Copying Data in an Excel Worksheet
    • Inserting and Deleting Rows and Columns
    Images and Shapes into Excel Worksheet
    • Inserting Excel Shapes
    • Formatting Excel Shapes
    • Inserting Images

    And, also –

    • Working with Excel SmartArt
    Basic work on Excel
    • Entering Values in a Worksheet and Selecting a Cell Range
    • Working with Numeric Data in Excel
    • Entering Text to Create Spreadsheet Titles

    Besides, there are –

    • Entering Date Values and using AutoComplete
    • Moving and Copying Cells with Drag and Drop
    • Using the Paste Special Command

    Also, it has –

    • Inserting and Deleting Cells, Rows, and Columns
    • Using Undo, Redo, and Repeat
    • Checking Your Spelling

    Further, it has –

    • Finding and Replacing Information from worksheet & workbook
    • Inserting Cell Comments
    • Working with Cell References
    • Working with the Forms Menu
    • Sorting, Subtotaling & Filtering Data
    • Copy & Paste Filtered Records

    Also, it has –

    • Using Conditional Formatting
    • Use of Data Validation
    Excel Formulas
    • Creating Basic Formulas in Excel
    • Relative V/s Absolute Cell References in Formulas
    • Understanding the Order of Operation
    • Entering and Editing Text and Formulas

    And, also –

    • Fixing Errors in Your Formulas
    • Formulas with Several Operators and Cell Ranges
    • Quiz
    Excel Functions
    • Introduction to excel functions
    • Learn about SUM() Function
    • Work with the MIN() and MAX() Functions
    • Get your hands on the AVERAGE() Function

    Further, it has –

    • Working with the COUNT() Function
    • Adjacent Cells Error in Excel Calculations
    • Use of AutoSum command
    • AutoSum shortcut key

    And, also –

    • Using the AutoFill Command to Copy Formulas
    • Quiz
    Working with Charts / Graphs
    • Creating a column Chart
    • Working with the Excel Chart Ribbon

    Also, the module has-

    • Adding and Modifying Data on an Excel Chart
    • Formatting an Excel Chart
    • Moving a Chart to another Worksheet
    • Resizing a Chart

    Besides, there are –

    • Changing a Chart’s Source Data
    • Adding Titles, Gridlines, and a Data Table
    • Formatting a Data Series and Chart Axis
    • Using Fill EffectsAnd, also –

    And, also –

    • Changing a Chart Type and Working with Pie Charts
    • Quiz
    Data Analysis & Pivot Tables
        • Why Pivot Tables
        • Structuring Source Data for Analysis in Excel

    In addition, it contains –

        • Creating a PivotTable
        • Navigating & Manipulating the Pivot Table Field List
        • Exploring Pivot Table Analyze & Design OptionsAdditionally, there is –
          • Selecting, Clearing, Moving & Copying Pivot Tables
          • Refreshing & Updating Pivot Tables
          • Dealing with Growing Source Data
          • Formatting Data with Pivot Tables
          • Enriching data with Pivot table calculated values & fields

          And, also –

          • Formatting and Charting a PivotTable
          • Pivot Table Case Study
          • Quiz
          Basic Macros
          • Automating Tasks with Macros
          • Recording a Macro
          • Playing a Macro and Assigning a Macro a Shortcut Key
          SQL & MongoDB for Business
          Introduction to SQL
          • What is a Database?
          • Why SQL?
          • All about SQL

          And also, it covers –

          • Difference between SQL & MongoDB
          • Different Structured Query languages
          • Why MySQL?
          • Installation of MySQL

          Besides, there are –

          • DDL
          • SQL Keywords
          • DCL
          • TCL
          • Database Vs Excel Sheets
          • relational and database schema

          Also, it has –

          • Foreign and Primary Keys
          • database manipulation, management, and administration
    2. NoSQL Databases :
      • Topics – What is HBase?
      • HBase Architecture
      • HBase Components,
      • Storage Model of HBase,

      And, also –

      • HBase vs RDBMS
      • Introduction to Mongo DB, CRUD
      • Advantages of MongoDB over RDBMS
      • Use cases
      First Step in SQL Database
      • Creating Database
      • Dropping Database
      • Also, it has –
      • Using Database
      • Introduction to Tables
      • Data types in SQL

      Further, the module has –

      • Creating a table
      • Dropping table
      • Coding best practices in SQL
      SQL Fundamental Statements
      • SELECT Statement
      • COUNT
      • ORDER BY


      • IN, NOT IN
      • NULL and NOT_NULL
      • Comparison Operators (=, >, >=, <, <=)
      • MySQL Warnings (Understand and Debug)
      Refining Selection
      • LIMIT
      • BETWEEN
      • BETWEEN – AND
      SQL Intermediate Statements
      • Multiple INSERT
      • GROUP BY
      • HAVING

      Further, it has –

      • WHERE vs HAVING
      • UPDATE
      • DELETE
      • And, also –
      • AS
      Aggregator Functions
      • Application of Group By
      • Count Function

      And, also –

      • MIN and MAX
      • Sum Function
      • Avg Function
      • Introduction to JOINs
      • INNER Join
      • OUTER Join
      • Full Join

      Also, it contains –

      • Left Join
      • Right Join


    SQL String Functions
    • Loading Data
    • CONCAT

    Besides, there are –


    And, also covering –

    Advance SQL
    • Local, Session, Global Variables
    • Timestamps and Extract
    • TO_CHAR

    Further, including –

    • Mathematical Functions and Operators
    • Operators and their precedence
    • String Functions and Operators

    Further, it has –

    • SubQuery
    • Self-Join
    • ALTER table

    Additionally, there are –

    • CASE
    • CAST
    • NULLIF

    And, also –

    • Check Constarints
    • Views
    • Import & Export
    Basics and CRUD Operation
    • Databases, Collection & Documents
    • Shell & MongoDB drivers
    • What is JSON Data

    And also,

    • Create, Read, Update, Delete
    • Working with Arrays
    • Understanding Schemas and Relations
    • What is MongoDB?
    • Charateristics, Structure and Features
    • MongoDB Ecosystem
    • Installation process
    • Connecting to MongoDB database

    Also, there is –

    • What are Object Ids in MongoDb
    • Data Formats in MongoDB
    • MongoDB Aggregation Framework
    • Aggregating Documents

    Besides, there is –

    • What are MongoDB Drivers?
    • Finding, Deleting, Updating, Inserting Elements
    TABLEAU for Business
    Introduction to TABLEAU
    • What is TABLEAU?
    • Why use TABLEAU?
    • Installation of TABLEAU
    • Connecting to data source
    • Navigating Tableau

    Also, there is –

    • Creating Calculated Fields
    • Adding Colors
    • Adding Labels and Formatting
    • Exporting Your Worksheet
    • Creating dashboard pages

    Additionally, there are –

    • Different charts on TABLEAU (Bar graphs, Line graphs, Scatter graphs, Crosstabs, Histogram, Heatmap, Treemaps, Bullet graphs, etc.)
    • Dashboard Tricks
    • Hands on exercises
    Data Types in Tableau
    • Aggregation and Granularity
    • Preattentive Processing
    • Length and Position
    • Reference Lines
    • Parameters
    • Tooltips

    Besides this, there is –

    • Data Over Time – Tableau
    • Implementation
    • Advance Table Calculations
    • Creating multiple joins in Tableau
    • Relationships vs Joins

    And, also –

    • 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
    Mapping & Analytics
      • Getting Started With Visual Analytics
      • Geospatial Data
      • Mapping Workspace

    Besides, it covers –

      • Map Layers
      • Custom Territories
      • Common Mapping Issues
      • Creating a Map, Working with Hierarchies
      • Coordinate points

    Further, it has

    Power BI for Business
    Introduction to Power BI
    • Why Power BI?
    • Account Types
    • Installing Power BI

    And, it also has –

    • Understanding the Power BI Desktop Workflow
    • Exploring the Interface of the Data Model
    • Understanding the Query Editor Interface
    Query Editor
    • Connecting Power BI Desktop to Source Files
    • Keeping & Removing Rows
    • Removing Empty Rows

    Further, it contains –

    • Create calculate columns
    • Make first row as headers
    • Change Data type
    • Rearrange the columns
    • Remove duplicates

    Besides, it covers –

    • Unpivot columns and split columns
    • Working with Filters
    • Appending Queries
    • Working with Columns
    • Replacing Values
    • Splitting Columns

    And, also –

    • Formatting Data & Handling Formatting Errors
    • Pivoting & Unpivoting Data
    • Query Duplicates vs References
    • Append Queries

    Moreover, it contains –

    • Merging Queries
    • DIM-Region Table
    • Understanding “Extract”
    • Basic Mathematical Operations
    • Introduction
    • Line Charts
    • Pie Chart
    • Bar Charts
    • Stacked bar Chart

    Also, it covers –

    • Clustered Column Chart
    • Combo Chart
    • Treemap Chart

    Further, covering –

    • funnel Chart
    • Scatter Chart
    • Gauge Card
    • Matrix

    Additionally, covering –

    • Table
    • Slicers
    • KPIs
    • Maps

    In addition, it has –

    • Text boxes – Shapes – Images
    Working with Power BI
    • Working with Time series
    • Understanding aggregation and granularity
    • Filters and Slicers in Power BI
    • Maps, Scatterplots and BI Reports

    Further, it includes –

    • Creating a Customer Segmentation
    • Analyzing the Customer
    • Segmentation Dashboard
    • Waterfall, Map Visualization

    Also, it has –

    • Pie and Tree Map
    • Include and Exclude
    • Categories with no Data
    Data Models: Data and Relationship
    • Understanding Relationships
    • Many-to-One & One-to-One

    Besides, it has –

    • Cross Filter Direction & Many-to-Many
    • M-Language vs DAX (Data Analysis Expressions)
    • Basics of DAX
    • DAX Data Types

    Also, it contains –

    • DAX Operators and Syntax
    • Importing Data for DAX Learning
    • Resources for DAX Learning

    Further, including –

    • M vs DAX
    • Understanding IF & RELATED
    • Create a Column
    • Rules to Create Measures
    • Calculated Columns vs Calculated Measures
    • Understanding CALCULATE & FILTER

    Further, including –

    • Understanding “Data Category”
    Time Intelligence
    • Create Date Table in M
    • Create Date Table in DAX
    • Display Last Refresh Date

    And, also –

    • Creating your first report
    • Modelling Basics to Advance
    • Modelling and Relationship
    • Ways of creating relationship

    Further, it contains –

    • Normalization – De Normalization
    • OLTP vs OLAP
    • Star Schema vs Snowflake Schema

    Enchase your skills by Working on live projects

    25+ Industry level projects included in this course which will help you a lot.

    Expert reviews on our data science course

    Industry expert recommendations on
    our Data science and AI course

    Get to learn more about our data science course

    Enquire for Program to know more on career transition into  data science and AI domain

      Data science and AI Program For Fresh Graduates & College Students

      50,000 + 18% GST

      The Data science and AI course with internship is specifically designed for college students. Most importantly, with this best data science course, you get industrial experience that helps you get hired in product-based companies and startups. In addition, you will get shareable internship certification.


      Additional information

      Select Batch

      Weekend Batch: 27th November, Sunday, Weekday Batch: 8th November, Monday

      Frequently Asked Questions

      What is the data science course duration and what modules are covered?

      The data science course duration is 6 months. The amount of time you spend learning will vary depending on your schedule (weekdays/weekends). Furthermore, you will be able to learn about a variety of topics through numerous courses. 

      Most importantly, begin with the fundamentals of programming, such as python and cloud fundamentals, then work your way up to Tensorflow-based deep learning, Natural Language Processing, and Artificial Neural Networks. Furthermore, you will become acquainted with AWS, Advanced Excel, MongoDB, Power BI, and Tableau.

      What are the different learning options available under the artificial intelligence and data science course in Skillslash?

      We provide both offline and online learning options in the majority of circumstances. Apart from that, we offer a blended learning programme tailored to the needs of working professionals. In addition, you can attend all theoretical courses in our Data science and AI course by participating in live online sessions in hybrid learning mode. On top of that, you will be doing some hands-on work on the industrial project site.

      Due to the pandemic, we can only offer full online classes via live sessions.  So,  you can converse with your instructor in real time, just as you would in a regular face-to-face class.  And, all practical sessions are currently conducted using cloud-based services.

      Are the lessons held during the week (on a daily basis)?

      We have two groups of students. One is a regular session that runs Monday through Friday, while the other is a weekend class that runs Saturday and Sunday. In addition, you may select one of these two sets at your leisure. However, whatever option you choose, you must remain with it for the duration of the course.

      Are the professors academic researchers (data science) or industry experts?

      In today’s job environment, we endeavour to ensure that all of our students are highly employable. As a result, we only hire teachers who have at least six years of experience teaching the courses in question.

      What are the prerequisites for enrolling in a Data science and AI course? Who can do data science course?

      We believe that every eager data science aspirant is capable of making a successful data science career move. So, no, we haven’t imposed any severe eligibility criteria. However, we assess candidates’ aptitude for the learning module based on their statistical knowledge and programming skills.

      On the other hand, the goal of this eligibility check is to provide the appropriate amount of guidance. Candidates with non-statistical or non-programming backgrounds will get additional assistance to learn Module 0. Aside from that, an applicant must meet the following requirements.

      • Currently enrolled in the last year of a bachelor’s or master’s degree programme at a college or university.
      • Candidates with a B.Tech, B.E, M.Tech, MCA, or MBA are favoured regardless of branch.

      Other students pursuing different degrees can, however, contact us for data science job advice. We will assist them in determining whether a data science career is right for them and how they can plan their data science career transition.

      What is the data science course fee at Skillslash?

      At Skillslash, we have a data science course fee of INR 50,000.In addition, you can also avail a scholarship programme under our Data science and AI course with internship.

      Is there a 100% scholarship available for COVID-affected candidates who apply for this course?

      You can get up to a 30% scholarship for the Fresh Graduates programme if you take an aptitude exam.

      What is the scholarship programme under artificial intelligence and data science course at Skillslash?

      To begin, each candidate will be offered the option to complete a 20-minute online aptitude test. So, if a candidate has a score of more than 65 percent on the aptitude exam, he or she will be eligible for a 30 percent reduction on the course fees. Furthermore, candidates who lost their jobs as a result of the COVID scenario, as well as mothers who want to start a profession, may be eligible for a scholarship of up to 100% based on their exam score.

      Is it necessary for me to pay all of the fees at once when I register?

      Most significantly, we offer two distinct ways to pay for your charges. Firstly, you will be asked to submit a one-time payment during the final registration process. Besides, you can also pay for the course during the course’s duration in monthly instalments. Aside from that, using credit cards can get you a lower EMI and an education loan.

      Are there any course fee discounts available with Skillslash’s Data science and AI course?

      You may be able to save up to 30% on your tuition if you are found eligible for a scholarship. In addition, we provide scholarships to Covid-affected candidates, unemployed candidates, and mothers returning to work after a break.

      What distinguishes the placement help programme under data science course from others?

      Here, you will get competent resume building support for selected job roles. Besides, mock interview tests for defined job roles provide suggestions for interview preparation. Furthermore, get job referrals for AI and other data science opportunities in product-based multinational corporations and startups. Special assistance in domain selection and career role based on your specific skill set is also included under this data science course.

      Do Skillslash solely help final-year students with their projects?

      This data science course is an internship-based learning programme in its entirety. Also, your internship will begin the day you register in the course. Students in their last year of college who need help with their industrial capstone project can enroll in this Data science and AI course.

      If I have to miss a class due to an emergency, will there be a make-up class available?

      Above all, we provide live online classes. Additionally, all of our students have access to video recordings of those classes. Besides, we  provide you with unrestricted access to these recorded sessions so that you can refer to them whenever you need theoretical assistance in your AI or ML career. As a result, if you miss any of the live classes, you won’t be disappointed. That said, we do, however, strongly encourage you to attend all live classes.

      Is the placement programme equivalent to campusing at a university or college?

      We presently do not offer any campusing benefits that are equivalent to those offered by universities. Instead, we provide you with a project experience certificate as well as job referrals to other product-based organisations in your selected domain.

      How many projects are included in this artificial intelligence and data science course?

      The Data science and AI course offers 15+ real-world projects from which you can choose a couple for case study learning based on the demands of your domain. Aside from that, during the following modules, you will have the opportunity to work on three industry projects (capstone projects) with a variety of MNCs and startups.

      What is the goal of the 'domain specialisation tracking' under the industrial project experience programme?

      Based on your topic knowledge, our industry experts faculty will aid you in choosing the ideal employment role. Furthermore, based on your existing work experience, you will be able to select the appropriate industrial project.

      What kind of certification does the Data science and AI course provide?

      The catch is that you will not receive any form of certification or academic degree if you attend the artificial and machine learning course. Instead, we provide a project management certificate that is recognized all over the world. Besides, direct certifications are also available from the company with which you completed your industrial project.

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