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Data Scientist Master's program - In Malaysia

The Boulevard Mid Valley City, Lingkaran Syed Putra 59200 Kuala Lumpur

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What are the learning objectives?

Data Science is among the most preferred career right now. with it's demand constantly increasing . all the leading training institude have started offering courses focused on developing data science skills among being and aspiring professionals.

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Who Should Take This Data Scientist Master's Program - in Malaysia?

The program requires an exposure to data science concepts in combination with some work experience in analytics.

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What are the prerequisites for this Data Science Training - in Malaysia?

Professional wishing to succeed in this dta science course should have basic knowladge of statistics Basic understanding of any programming language.

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What Type of Job will be suited after completing this Data Science Course - in Malaysia?

Sky the limits for data science professionals. Once the course is completed,you can expect a promissing position in any industry that run on analytics.

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Program Advisor

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Ramesh Kumar

IT CONSULTANT & TRAINER

Delivered more than 150 corporate batches at companies like Citi Bank, Royal Bank of Scotland, UOB Banks Singapore, Capgemini, United Health Group, Wirpo, IBM and many more

Course 1 Online Classroom

Python Open This Course Page

Python Certification will assist you in mastering the concepts of Python and its libraries like SciPy, Matplotlib, Scikit-Learn, Pandas, NumPy, Lambda functions, and Web Scraping. Learn how to write Python Programming for Big Data systems such as Spark and Hadoop.

Industry Projects *
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Learning Objective:
Learn about the history of Python and its various uses. Learn to use interpreters and also learn about how Python scripts work on UNIX/Windows.

Topic Covered:

  • History of Python
  • Why to use Python?
  • Starting Python
  • Interpreter PATH
  • Using the Interpreter
  • Python Scripts on UNIX/Windows

Hands-on: Learn about interpreters and Python Script.

Learning Objective:
Learn how to install Python distribution - Anaconda. Learn basic data types, strings & regular expressions.

Topic Covered:

  • Python Editors and IDEs
  • Install Anaconda

Hands-on: Install Anaconda - Python distribution

Learning Objective:
In this module, you will learn to convert messy text into something useful.

Topic Covered:

  • String Operations
  • Data Types & Variables
  • Commonly used Operators

Hands-on: Write Python code to implement string operations.

Learning Objective:
Learn the various data structures that are used in Python.

Topic Covered:

  • Arrays
  • Lists
  • Tuples
  • Dictionaries
  • Sets

Hands-on: Write Python Code to understand and implement Python Data Structures.

Learning Objective:
Learn all about loops and control statements in Python.

Topic Covered:

  • For Loop
  • While Loop
  • Break Statement
  • Next Statements
  • Repeat Statement
  • if, if…else Statements
  • Switch Statement

Hands-on: Write Python Code to implement loop and control structures in R.

Learning Objective:
Write user-defined functions in Python. Learn about Lambda function. Learn the object oriented way of writing classes & objects.

Topic Covered:

  • Writing your own functions (UDF)
  • Calling Python Functions
  • Functions with Arguments
  • Calling Python Functions by passing Arguments
  • Lambda Functions

Hands-on: Write Python Code to create your own custom functions without or with arguments. Know how to call them by passing arguments wherever required.

Learning Objective:
Learn to build modules and install packages.

Topic Covered:

  • The Import Statement
  • Module Search Path
  • Package Installation Ways

Hands-on: Write Python Code to create modules and execute them.

Learning Objective:
Learn about Regular Expression Objects, subexpressions, tips and tricks to implement while you code.

Topic Covered:

  • RE Objects
  • Pattern matching
  • Parsing data
  • Subexpressions
  • Complex substitutions
  • RE tips and tricks

Hands-on: Write Python Code to use Regular Expression and match pattern, parse data and so on.

Learning Objective:
Gain knowledge on OOPs to code easily and efficiently. Learn to construct classes and define objects."

Topic Covered:

  • Introduction to Python Classes
  • Defining Classes
  • Initializers
  • Instance Methods
  • Properties
  • Class Methods and Data
  • Static Methods
  • Private Methods and Inheritance
  • Module Aliases

Hands-on: Write Python code to construct a class and define objects.

Learning Objective:
Study Use Cases to explore Python

Topic Covered:

  • Use Case

Hands-on: Use cases covering conditional statements, functions, classes, modules, regular expressions.

Course 2 Online Classroom

Machine Learning Open This Course Page

Machine Learning Course will make you an expert in machine learning, a form of artificial intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming.

Industry Projects *
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Topic Covered:

  • Various machine learning algorithms in Python
  • Apply machine learning algorithms in Python

Topic Covered:

  • How to select the right data
  • Which are the best features to use
  • Additional feature selection techniques
  • A feature selection case study
  • Preprocessing Introduction
  • Preprocessing Scaling Techniques
  • How to preprocess your data
  • How to scale your data
  • Feature Scaling Final Project

Topic Covered:

  • Highly efficient machine learning algorithms
  • Bagging Decision Trees
  • The power of ensembles
  • Random Forest Ensemble technique
  • Boosting – Adaboost
  • Boosting ensemble stochastic gradient boosting
  • A final ensemble technique

Topic Covered:

  • Introduction Model Tuning
  • Parameter Tuning GridSearchCV
  • A second method to tune your algorithm
  • How to automate machine learning
  • Which ML algo should you choose
  • How to compare machine learning algorithms in practice

Topic Covered:

  • Neural Networks introduction
  • What is deep learning
  • What is one hot encoding
  • How to implement one hot encoding
  • How to handle missing values
  • How to impute missing values
  • Introducing the MNIST dataset

Topic Covered:

  • Programming a neural network in tensorflow
  • Programming a neural network – multilayer perceptron in tensorflow

Topic Covered:

  • Introduction to keras – a convient way to code neural networks
  • What is a convolutional neural network
  • How does a cnn work

Topic Covered:

  • Creating a convolutional neural network from scratch
  • What are RNNs – Introduction to RNNs
  • Recurrent Neural Networks rnn in python
  • LSTMs for beginners – understanding LSTMs
  • long short term memory neural networks lstm in python
Course 3 Online Classroom

Deep Learning Open This Course Page

Data Science with R certification course makes you an expert in data analytics using the R programming language. This online training enables you to take your Data Science skills into a variety of companies, helping them

Industry Projects *
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Topic Covered:

  • What are the Limitations of Machine Learning?
  • What is Deep Learning?
  • Advantage of Deep Learning over Machine learning
  • Reasons to go for Deep Learning
  • Real-Life use cases of Deep Learning

Topic Covered:

  • History of AI
  • Modern era of AI
  • How is this era of AI different?
  • Transformative Changes
  • Role of Machine learning & Deep Learning in AI
  • Hardware for AI (CPU vs. GPU vs. TPU)
  • Software Frameworks for AI
  • Deep Learning Frameworks for AI
  • Key Industry applications of AI

Topic Covered:

  • Overview of important python packages for Deep Learning

Topic Covered:

  • What is Tensor Flow?
  • Tensor Flow code-basics
  • Graph Visualization
  • Constants, Placeholders, Variables
  • Tensorflow Basic Operations
  • Linear Regression with Tensor Flow
  • Logistic Regression with Tensor Flow
  • K Nearest Neighbor algorithm with Tensor Flow
  • K-Means classifier with Tensor Flow
  • Random Forest classifier with Tensor Flow

Topic Covered:

  • Quick recap of Neural Networks
  • Activation Functions, hidden layers, hidden units
  • Illustrate & Training a Perceptron
  • Important Parameters of Perceptron
  • Understand limitations of A Single Layer Perceptron
  • Illustrate Multi-Layer Perceptron
  • Back-propagation – Learning Algorithm
  • Understand Back-propagation – Using Neural Network Example
  • TensorBoard

Topic Covered:

  • What is Deep Learning Networks?
  • Why Deep Learning Networks?
  • How Deep Learning Works?
  • Feature Extraction
  • Working of Deep Network
  • Training using Backpropagation
  • Variants of Gradient Descent
  • Types of Deep Networks
  • Feed forward neural networks (FNN)
  • Convolutional neural networks (CNN)
  • Recurrent Neural networks (RNN)
  • Generative Adversal Neural Networks (GAN)
  • Restrict Boltzman Machine (RBM)

Topic Covered:

  • Introduction to Convolutional Neural Networks
  • CNN Applications
  • Architecture of a Convolutional Neural Network
  • Convolution and Pooling layers in a CNN
  • Understanding and Visualizing a CNN
  • Transfer Learning and Fine-tuning Convolutional Neural Networks

Topic Covered:

  • Intro to RNN Model
  • Application use cases of RNN
  • Modelling sequences
  • Training RNNs with Backpropagation
  • Long Short-Term Memory (LSTM)
  • Recursive Neural Tensor Network Theory
  • Recurrent Neural Network Model

Topic Covered:

  • What is Restricted Boltzmann Machine?
  • Applications of RBM
  • Collaborative Filtering with RBM
  • Introduction to Autoencoders & Applications
  • Understanding Autoencoders

Topic Covered:

  • Define TFlearn
  • Composing Models in TFlearn
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with TFlearn
  • Customizing the Training Process
  • Using TensorBoard with TFlearn
  • Use-Case Implementation with TFlearn

Topic Covered:

  • Define Keras
  • How to compose Models in Keras
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with Keras
  • Customizing the Training Process
  • Using TensorBoard with Keras
  • Use-Case Implementation with Keras
  • Intuitively building networks with Keras

Topic Covered:

  • Computer Vision
  • Text Data Processing
  • Image processing
  • Audio & video Analytics
  • Internet of things (IOT)
Course 4 Online Classroom

Natural Language Processing Open This Course Page

Data Science with R certification course makes you an expert in data analytics using the R programming language. This online training enables you to take your Data Science skills into a variety of companies, helping them

Industry Projects *
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Topic Covered:

  • Overview of Natural Language Processing
  • Machine learning methods
  • Deep learning methods.

Learning Objective:
Learn about the interaction between computers and human beings which gives computers the ability to understand human speech with the help of machine learning. Understand the concept behind tokenization and normalization

Topic Covered:

  • Introduction to Regular Expressions
  • Tokenization of text
  • Normalization of text
  • Substituting and correcting tokens
  • Applying Zipf's law to text
  • Applying similarity measures using the Edit Distance Algorithm
  • Applying similarity measures using Jaccard's Coefficient
  • Applying similarity measures using Smith Waterman

Hands-on: Apply various similarity measures to strings using NLTK

Learning Objective:
Understand the preprocessing tasks or the computations that can be performed on natural language text. Learn about the ways to calculate word frequencies, the Maximum Likelihood Estimation (MLE) model, interpolation on data, and so on

Topic Covered:

  • Understanding word frequency
  • Applying smoothing on the MLE model
  • Develop a backup mechanism for MLE
  • Data Interpolation
  • Language modelling using metropolis hastings
  • Gibbs sampling in language processing

Hands-on: Implement Maximum Likelihood Estimation in NLTK and perform language modelling

Learning Objective:
Learn about stemming and lemmatization, stemmer and lemmatizer for non-English languages, developing a morphological analyzer and morphological generator using machine learning tools, search engines, and many such concepts

Topic Covered:

  • Introducing Morphology
  • Understanding stemmer
  • Lemmatization
  • Morphological analyzer
  • Morphological generator

Hands-on: Perform preprocessing on the original text in order to implement or build an application. Implement stemming, lemmatization, and morphological analysis and generation in NLTK

Learning Objective:
Understand the process of finding whether a character sequence, written in natural language, is in accordance with the rules defined in formal grammar. Also, learn about the process of breaking the sentences into words or phrase sequences and providing them a particular component category (noun, verb, preposition, and so on)

Topic Covered:

  • Introducing Parsing
  • Treebank construction
  • Extracting Context Free Grammar (CFG) rules from Treebank
  • CYK chart parsing algorithm
  • Earley chart parsing algorithm

Hands-on: Implement Context-free Grammar, Probabilistic Context-free Grammar, the CYK algorithm and the Earley algorithm

Learning Objective:
Understand the process of determining the meaning of character sequences or word sequences which may be used for performing the task of disambiguation

Topic Covered:

  • Introducing semantic analysis
  • Named-entity recognition (NER)
  • NER system using the HMM
  • Training NER using machine learning toolkits
  • NER using POS tagging
  • Generation of the synset id from Wordnet
  • Disambiguating senses using Wordnet

Learning Objective:
Understand the process of determining the sentiments behind a character sequence. It may be used to determine whether the speaker or the person expressing the textual thoughts is in a happy or sad mood, or it represents a neutral expression

Topic Covered:

  • Introducing sentiment analysis
  • Sentiment analysis using NER
  • Sentiment analysis using machine learning
  • Evaluation of the NER system

Learning Objective:
Understand the process of retrieving information (for example, the number of times the word "Analysis" has appeared in the document) corresponding to a query that has been made by the user

Topic Covered:

  • Introducing information retrieval
  • Stop word removal
  • Information retrieval using a vector space model
  • Vector space scoring and query operator interactions
  • Text summarization

Hands-on: Implement text summarization, question-answering systems, and vector space models

Learning Objective:
Understand the process of determining contextual information that is useful for performing other tasks, such as anaphora resolution (AR), NER, and so on

Topic Covered:

  • Introducing discourse analysis
  • Discourse analysis using Centering Theory
  • Anaphora resolution

Hands-on: Use NLTK to implement first order predicate logic using UML diagrams

Learning Objective:
Learn to analyze whether a given NLP system produces the desired result or not and the desired performance is achieved or not which may be performed automatically using predefined metrics, or it may be performed manually by comparing human output with the output obtained by an NLP system

Topic Covered:

  • The need for the evaluation of NLP systems
  • Evaluation of IR Systems
  • Metrics for error identification
  • Metrics based on lexical matching
  • Metrics based on syntactic matching
  • Metrics using shallow semantic matching
Course 5 Online Classroom

Tableau Course Open This Course Page

Mildaintrainings Tableau Training - This hands-on, instructor-led course teach you how to transform raw data into interactive and shareable dashboards using Tableau. Our Tableau Course covers the necessary analytical skills to Advanced data visualizations By incorporating real-world use-case scenarios, labs, and exercises. Some of the topics included are Data Blending, Data Mapping, Graphs, creation of charts, and LOD expression by using different versions of Tableau, such as Tableau Desktop, Tableau Reader, and Tableau Public. You also learn the process to integrate Tableau with R and Big Data in this Tableau certification course.

Industry Projects *
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Topic Covered:

  • What is data visualization?
  • Comparison and benefits against reading raw numbers
  • Real use cases from various business domains
  • Some quick and powerful examples using Tableau without going into the technical details of Tableau
  • Installing Tableau
  • Tableau interface
  • Connecting to DataSource
  • Tableau data types
  • Data preparation

Topic Covered:

  • Installation of Tableau Desktop
  • Architecture of Tableau
  • Interface of Tableau (Layout, Toolbars, Data Pane, Analytics Pane, etc.)
  • How to start with Tableau
  • The ways to share and export the work done in Tableau

Topic Covered:

  • Connection to Excel
  • Cubes and PDFs
  • Management of metadata and extracts
  • Data preparation
  • Joins (Left, Right, Inner, and Outer) and Union
  • Dealing with NULL values, cross-database joining, data extraction, data blending, refresh extraction, incremental extraction, how to build extract, etc.

Topic Covered:

  • Mark, highlight, sort, group, and use sets (creating and editing sets, IN/OUT, sets in hierarchies)
  • Constant sets
  • Computed sets, bins, etc.

Topic Covered:

  • Filters (addition and removal)
  • Filtering continuous dates, dimensions, and measures
  • Interactive filters, marks card, and hierarchies
  • How to create folders in Tableau
  • Sorting in Tableau
  • Types of sorting
  • Filtering in Tableau
  • Types of filters
  • Filtering the order of operations

Topic Covered:

  • Using Formatting Pane to work with menu, fonts, alignments, settings, and copy-paste
  • Formatting data using labels and tooltips
  • Edit axes and annotations
  • K-means cluster analysis
  • Trend and reference lines
  • Visual analytics in Tableau
  • Forecasting, confidence interval, reference lines, and bands

Topic Covered:

  • Working on coordinate points
  • Plotting longitude and latitude
  • Editing unrecognized locations
  • Customizing geocoding, polygon maps, WMS: web mapping services
  • Working on the background image, including add image
  • Plotting points on images and generating coordinates from them
  • Map visualization, custom territories, map box, WMS map
  • How to create map projects in Tableau
  • Creating dual axes maps, and editing locations

Topic Covered:

  • Calculation syntax and functions in Tableau
  • Various types of calculations, including Table, String, Date, Aggregate, Logic, and Number
  • LOD expressions, including concept and syntax
  • Aggregation and replication with LOD expressions
  • Nested LOD expressions
  • Levels of details: fixed level, lower level, and higher level
  • Quick table calculations
  • The creation of calculated fields
  • Predefined calculations
  • How to validate

Topic Covered:

  • Creating parameters
  • Parameters in calculations
  • Using parameters with filters
  • Column selection parameters
  • Chart selection parameters
  • How to use parameters in the filter session
  • How to use parameters in calculated fields
  • How to use parameters in the reference line

Topic Covered:

  • Dual axes graphs
  • Histograms
  • Single and dual axes
  • Box plot
  • Charts: motion, Pareto, funnel, pie, bar, line, bubble, bullet, scatter, and waterfall charts
  • Maps: tree and heat maps
  • Market basket analysis (MBA)
  • Using Show me
  • Text table and highlighted table

Topic Covered:

  • Building and formatting a dashboard using size, objects, views, filters, and legends
  • Best practices for making creative as well as interactive dashboards using the actions
  • Creating stories, including the intro of story points
  • Creating as well as updating the story points
  • Adding catchy visuals in stories
  • Adding annotations with descriptions; dashboards and stories
  • What is dashboard?
  • Highlight actions, URL actions, and filter actions
  • Selecting and clearing values
  • Best practices to create dashboards
  • Dashboard examples; using Tableau workspace and Tableau interface
  • Learning about Tableau joins
  • Types of joins
  • Tableau field types
  • Saving as well as publishing data source
  • Live vs extract connection
  • Various file types

Topic Covered:

  • Introduction to Tableau Prep
  • How Tableau Prep helps quickly combine join, shape, and clean data for analysis
  • Creation of smart examples with Tableau Prep
  • Getting deeper insights into the data with great visual experience
  • Making data preparation simpler and accessible
  • Integrating Tableau Prep with Tableau analytical workflow
  • Understanding the seamless process from data preparation to analysis with Tableau Prep

Topic Covered:

  • Introduction to R language
  • Applications and use cases of R
  • Deploying R on the Tableau platform
  • Learning R functions in Tableau
  • The integration of Tableau with Hadoop
Course 6 Online Classroom

Big Data Course Open This Course Page

The core objective of this course is to get a comprehensive understanding of large volumes of data, including structured, unstructured, text, social media, video, audio, image, bot, and device log data and mastering technologies used to store, manipulate, analyse, and derive insights using statistics, Machine Learning algorithms, and Big Data tools.

Industry Projects *
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Topic Covered:

  • Introduction to NoSQL Databases
  • Introduction to NoSQL and MongoDB
  • MongoDB installation
  • Importance of NoSQL
  • CRUD operations
  • Data modeling and schema design
  • Data management and administration
  • Data indexing and aggregation
  • MongoDB security
  • Working with unstructured data

Topic Covered:

  • Introduction to statistics
  • Logistic regression
  • Decision trees and random forest

Topic Covered:

  • Data Analytics in Excel
    • Concepts of finance
    • Concepts of economics
    • Hands-on: Inferential statistics, descriptive statistics, simple and multivariate regression, and confidence intervals
  • Data Analytics Using SQL
    • Introduction to MySQL
    • Working with MySQL and MySQL IDE: Installation and setup
    • Introduction to SQL queries: DDL queries (create and select) and DML queries (alter, insert, etc.)
    • Working with joins, group, and filter
    • Writing complex SQL queries for data retrieval and the import and export of data and database tables

Topic Covered:

  • Introduction to Python
  • Python basic constructs
  • OOPs in Python
  • NumPy for mathematical computing
  • SciPy for scientific computing
  • Data manipulation
  • Data visualization with Matplotlib
  • Implementing statistical algorithms using Python

Topic Covered:

  • Hadoop installation and setup
  • Introduction to Big Data and Hadoop
  • Understanding HDFS and MapReduce
  • Deep dive into MapReduce
    • Introduction to Hive
    • Advanced Hive and Impala
    • Introduction to Pig
    • Flume and Sqoop

Topic Covered:

  • Scala programming
  • Spark framework
  • RDD in Spark
  • DataFrames and Spark SQL
  • Machine Learning using Spark (MLlib)

Topic Covered:

  • Introduction to PySpark
  • Who uses PySpark?
  • Why Python for Spark?
  • Values, Types, Variables
  • Operands and Expressions
  • Conditional Statements
  • Loops
  • Numbers
  • Python files I/O Functions
  • Strings and associated operations
  • Sets and associated operations
  • Lists and associated operations
  • Tuples and associated operations
  • Dictionaries and associated operations

Topic Covered:

  • Functions
  • Lambda Functions
  • Global Variables, its Scope, and Returning Values
  • Standard Libraries
  • Object-Oriented Concepts
  • Modules Used in Python
  • The Import Statements
  • Module Search Path
  • Package Installation Ways

Topic Covered:

  • Why model tuning?
  • What is model tuning?
  • What are parameters
  • What are Hyper-parameters
  • What is Hyper-parameter tuning?
  • Types of Hyper parameter tuning:
  • Grid Search
  • Random Search

Topic Covered:

  • Why Ensemble Learning?
  • What is Ensemble Learning?
  • Model Error
  • Bias
  • Variance
  • Reducing Model Error
  • Different Types of Ensemble Learning
  • Bagging
  • Boosting
  • Stacking

Topic Covered:

  • What is Model Deployment
  • Model Deployment Strategy
  • Steps in Model Deployment
  • Create a model
  • Save it
  • Load in in a web server/ web api
  • Make Predictions

Topic Covered:

  • Spark Components & its Architecture
  • Spark Deployment Modes
  • Spark Web UI
  • Introduction to PySpark Shell
  • Submitting PySpark Job
  • Writing your first PySpark Job Using Jupyter Notebook
  • What is Spark RDDs?
  • Stopgaps in existing computing methodologies
  • How RDD solve the problem?
  • What are the ways to create RDD in PySpark?
  • RDD persistence and caching
  • General operations: Transformation, Actions, and Functions
  • Concept of Key-Value pair in RDDs
  • Other pair, two pair RDDs
  • RDD Lineage
  • RDD Persistence
  • WordCount Program Using RDD Concepts
  • RDD Partitioning & How it Helps Achieve Parallelization
  • Passing Functions to Spark

Topic Covered:

  • Need for Spark SQL
  • What is Spark SQL
  • Spark SQL Architecture
  • SQL Context in Spark SQL
  • User-Defined Functions
  • Data Frames
  • Interoperating with RDDs
  • Loading Data through Different Sources
  • Performance Tuning
  • Spark-Hive Integration

Topic Covered:

    Introduction to Spark Streaming Features of Spark Streaming Spark Streaming Workflow StreamingContext Initializing Discretized Streams (DStreams) Input DStreams, Receivers Transformations on DStreams DStreams Output Operations Describe Windowed Operators and Why it is Useful Stateful Operators Vital Windowed Operators Twitter Sentiment Analysis Streaming using Netcat server WordCount program using Kafka-Spark Streaming

Topic Covered:

  • Introduction to Machine Learning- What, Why and Where?
  • Use Case
  • Types of Machine Learning Techniques
  • Why use Machine Learning for Spark?
  • Applications of Machine Learning (general)
  • Applications of Machine Learning with Spark
  • Introduction to MLlib
  • Features of MLlib and MLlib Tools
  • Various ML algorithms supported by MLlib
  • Supervised Learning Algorithms
  • Unsupervised Learning Algorithms
  • ML workflow utilities

Topic Covered:

  • Apache Flume and Apache Kafka
  • Spark Streaming
  • Case Study: Spark vs Kafka and when to use them

Topic Covered:

  • Creation of multi-node cluster setup using Amazon EC2
  • Hadoop Administration: Cluster configuration
  • Hadoop Administration: Maintenance, monitoring, and troubleshooting
  • Implementing security using Kerberos
  • Maintenance, monitoring, alerting, and troubleshooting Big Data solutions

Topic Covered:

  • What is data warehousing? What is data mining? Use cases and applications
  • Creating data models for large data warehouses
  • Different types of data models: Star, snowflake, and hybrid; which is the right model?
  • Integration of Hadoop and Spark with an ETL tool
  • Building workflows using Informatica for the integration with HDFS, Hive, MapReduce, etc.
  • Performance Tuning of ETL systems for processing large datasets

Topic Covered:

  • Introduction to data visualization and the power of Tableau
  • Architecture of Tableau
  • Working with metadata and data blending
  • Creation of sets
  • Working with filters
  • Organizing data and visual analytics
  • Working with mapping
  • Working with calculations and expressions
  • Working with parameters
  • Charts and graphs
  • Dashboards and stories
  • Tableau Prep
  • Integration of Tableau with Big Data tools like Hadoop and Spark

Topic Covered:

  • Marketing, Web, and Social Media Analytics
  • Fraud and Risk Analytics
  • Supply Chain and Logistics Analytics
  • HR Analytics

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FAQs

Ans. Data science doesn't need a word of mouth today. It has become so popular that freshers, as well as professionals, don't think twice before investing in a course that helps them bag a job in the area. The need for data science professionals lies in the very fact that different aspects of human life are transformed into data using electronic devices. You may not feel it but it happens, all the time. And over the years, this data has only become instrumental in making business decisions. They need a team to handle and arrange the large pool of data in a centralized manner. This makes data science a hot career option to opt for.

Ans. The answer to this question might vary from company to company but mostly it is a yes. As data scientists are considered to be decision-makers, they are among the most valued employees of any company. The same reflects in the salary they get. Apart from the salary, there might be lucrative incentives, depending on the company and its position in the market. In a nutshell, data scientists get handsome packages that most professionals dream of. As per a report, by the end of 2030, 50% of the total Indian economy is estimated to be constituted alone by the data-driven digital market. Therefore, it can be said that this decade will belong to data scientists and the same will be notable in their paychecks.

Ans. To become a successful data scientist, you must have all the essential skills for pursuing data science as a career. The one-line answer might be something like, join a course and learn these skills but that is not the complete answer. The secret lies in having basics clear. In case it is not, there is nothing to worry about. You must have an idea of what subjects to focus on and you're set. The first step is to revise mathematics concepts with the main focus on linear algebra, calculus, matrix theory, statistics, probability analysis, and numerical analysis but avoid overdoing. In addition, you can start reading data science blogs, learn fundamentals, and then think of joining a data science program at a reputed training institute. Many training centres in NCR offer job-oriented data science courses.

Ans. Slowly but steadily, data science has been covering all industries which set the bar even higher. Employers expect professionals with a broader set of skills so that they can easily adapt to the fluctuating job demands whenever needed. The essential skills for data science professionals include coding, statistics, and domain knowledge. Coding skills include a thorough knowledge of R/python, preferably python with the ability to reuse codes, coding tests on SQL as 99% of the companies use it, excellent knowledge and command over Excel, and sound Tableau skills. Apart from this, data scientists must be well versed in maths, stats, ML algorithms, and domain knowledge.

Ans. The main role of data scientists is to extract useful insights which is also the challenging part. As a data scientist, you are responsible for managing a large pool of data in a centralized folder. This structured data will further be used for business growth by improving customer experience. Considering the gap present in the market, the demand for skilled data science professionals is constantly growing as they are the ones who can handle these demands. To acquire new customers and improve their experience with a brand or product, the company needs to personalize customer experience to a completely different level by gathering useful insights. Considering the large amounts of data, it can be said that a data scientist can spell success or failure for a brand based on his or her expertise. Therefore, the biggest challenge is honing the skill of creating insights.

Ans. The world is running on data and it is evident that data science has become an integral part of the world economy. As the role of data is not going to shrink, the future of data science looks unbelievably bright. Data science builds brands these days. Useful insights help companies read their consumer behavior and create products that add value to their lives. As customers can make or break a brand, customer data plays an essential role and will continue to do the same in the future. Another great part of being a data science professional is being able to work in switching roles in various industries. Data science covers almost all sectors and is constantly growing. This fact positions it among the most secure career options of this decade.

Ans. Data science and statistics both are used for analyzing data which sometimes makes people confused over their similarities and differences. The first thing to understand here is, they are not completely different. However, there is always a fine line that separates them from one another. Both analysis techniques might be used for data analysis but they differ in terms of the amount of data that can be handled. While data science can deal with an enormous amount of data, the statistics technique usually focuses on small chunks. Data science uses a predictive model while statistics uses a simple linear model for solving problems. Both the techniques might also vary in terms of the background study required, problems they solve, and potential applications.

Ans. Preparing for an interview depends on the position you're applying for. While freshers may do great by simply revising and practicing machine learning algorithms, python programming, and impressive presentation, for a position like that of a project manager, the level of preparation must be different. Apart from the required technical skills, the interviewer will expect you to have commendable experience in your domain and you must be able to independently handle projects. Here are some interview questions that will help.

Ans. Yes, the program is available online. In fact, following the pandemic, we have made all our programs available online. Once you register yourself, you can pay using any of the following modes:

  • Cash
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  • Debit Card
  • Credit Card
  • Net Banking
  • UPI payment (Phonepe, Google pay, etc.)
  • Online wallet (Paytm, Phonepe, etc.)
  • Visa
  • Mastercard

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