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Master in Data Science Course

Data Science Course from SMEClabs will make you a competent professional in the IT sector. In this century data certainly plays a huge role in everything. A proper collection of data with periodic analysis of it, evaluating them in desired levels using algorithms, seeking answers that would be the route maps to achieve success and the whole process is collectively known as Data Science. Ranging from Banks to Hospitals, Search Engines to Websites, Advertisements to Route planners, gamers to Augmented Reality, etc.
Everyone depends on data science in one way or the other for decision-making to take steps that get you one step higher and faster. Python being an open source with libraries such as Pandas, NumPy, SciPy, etc makes Python perfect for Data Science. Python plays a major role in data science, and SMEClabs will train you in this to make you a professional in this sector.
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Data Analytics Course Kochi

Data Science Courses

Master in Data Science

Difficulty - Beginner / No experience | Eligibility - Any Degree, Diploma, Graduates | Mode - Offline - 240 Hours / OnDemand / Hybrid
Detailed Syllabus | Enroll Now

Master in Data Science Level Requirements:
  • Degree | Diploma
  • Basics of Python Programming
  • Basics of Java
  • Basics of SQL
Data Science Course Master Advanced

Syllabus for Data Science Course - Master Level

  • Course Introduction
  • Programming
  • Introduction
  • Sample or Population Data?
  • The Fundamentals of Descriptive Statistics
  • Measures of Central Tendency, Asymmetry, and Variability
  • Practical Example: Descriptive Statistics
  • Distributions
  • Estimators and Estimates
  • Confidence Intervals Lesson
  • Practical Example: Inferential Statistics
  • Hypothesis Testing: Introduction
  • Practical Example: Hypothesis Testing
  • The Fundamentals of Regression Analysis
  • Assumptions for Linear Regression Analysis
  • Dealing with Categorical Data
  • Practical Example: Regression Analysis
  • Python Basics
  • Python Data Structures
  • Python Programming Fundamentals
  • Working with Data in Python
  • Data Science Overview
  • Data Analytics Overview
  • Statistical Analysis and Business Applications
  • Python Environment Setup and Essentials
  • Mathematical Computing with Python (NumPy)
  • Data Manipulation with Pandas
  • Machine Learning with Scikit–Learn
  • Natural Language Processing with Scikit Learn
  • Data Visualization in Python using Matplotlib
  • Web Scraping with Beautiful Soup
  • Working with NumPy Arrays
  • Introduction to Artificial Intelligence and Machine Learning
  • Data Wrangling and Manipulation
  • Supervised Learning
  • Feature Engineering
  • Supervised Learning Classification
  • Unsupervised Learning
  • Time Series Modeling
  • Ensemble Learning
  • Recommender Systems
  • Text Mining
  • Introduction to Artificial Intelligence and Machine Learning in NLP
  • Perform various Text Analysis, Summarization and Extraction, Sentiment Mining, Text Classification, Text Summarization, Information Extraction etc.
  • Create a basic speech recognizer to convert speech to text
  • Perform advanced Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition
  • Implement emotion recognition/sentiment analysis etc. using real time speech recognitions.
  • Introduction to SQL
  • Database Normalization and Entity-Relationship (ER) Mode
  • Installation configurations to setup MySQL
  • Understanding Database and Tables
  • Learn Operators, Constraints, and Data Types
  • Understanding functions, Subqueries, Operators, and Derived Tables in SQL
  • Routing: Flask’s routing system maps URLs to Python functions, allowing you to define your application’s URL structure.
  • Templates: Flask uses Jinja2 as its template engine, allowing you to easily generate dynamic HTML pages.
  • Forms: Flask’s form handling makes it easy to process user input and validate data.
  • Sessions and cookies: Flask’s session and cookie management features allow you to store user data and maintain state across multiple requests.
  • Database integration: Flask can be used with a variety of databases, including MySQL
  • RESTful APIs: Flask is a popular choice for building RESTful APIs due to its simplicity and flexibility.
  • Extensions: Flask has a wide range of extensions available for adding functionality to your application, including Flask-WTF, Flask-Security, and Flask-Mail.
  • Flask-Login: Flask-Login is an extension for handling user authentication and authorization in Flask applications.
  • Flask-RESTful: Flask-RESTful is an extension for building RESTful APIs with Flask, providing additional features for API development.
  • Deployment: Flask can be deployed to a variety of environments, including traditional web hosting, cloud-based services, and containers.
  • Construct Python Micro services with FastAPI
  • Understanding FastAPI and Uvicorn
  • Installation and Creating Your First API
  • Path Parameters
  • Query Parameters
  • Combining Path and
  • Query Parameters
  • Query parameter and string
  • Path parameter and numeric validation
  • Body multiple parameters, body field, body nested models
  • Declare request example data
  • Cookie parameters
  • Response status codes
  • Handling errors
  • JSON compatible encoder, security
  • Bigger applications – multiple files
  • Testing and debugging
  • Models: Django’s object-relational mapping (ORM) tool is used to define models, which are Python classes that represent database tables.
  • Views: Views are Python functions that handle HTTP requests and return HTTP responses.
  • Templates: Django’s template system allows you to define the structure and layout of your web pages.
  • Forms: Django provides a powerful form handling system, which makes it easy to process user input and validate data.
  • Admin site: Django comes with a built-in admin site, which provides an easy-to-use interface for  managing your application’s data.
  • Authentication: Django provides built-in authentication mechanisms to handle user authentication and authorization.
  • Middleware: Middleware is a mechanism in Django that allows you to process requests and responses before they are handled by views.
  • URLs: Django’s URL routing system allows you to map URLs to views and organize your application’s URLs.
  • Testing: Django provides a built-in testing framework that makes it easy to write tests for your application.
  • Deployment: Django can be deployed to a variety of environments, including traditional web hosting, cloud-based services, and containers.
  • Introduction to Hadoop, Architecture, Administration and Components
  • Understanding NoSQL Databases HBase
  • Understand basic object-oriented programming methodologies in Scala
  • Introduction to Scala, Basics of Functional Programming, Case Objects and Classes Collections
  • Introduction to Spark, Work with RDD in Apache Spark, processing real time data
  • Perform DataFrame operations in Spark using SQL queries
  • Spark MLib Modelling Big Data with Spark
  • Introduction to Spark GraphX
  • Introduction to Hadoop, Architecture, Administration and Components
  • Understanding NoSQL Databases HBase
  • Understand basic object-oriented programming methodologies in Scala
  • Introduction to Scala, Basics of Functional Programming, Case Objects and Classes Collections
  • Introduction to Spark, Work with RDD in Apache Spark, processing real time data
  • Perform DataFrame operations in Spark using SQL queries
  • Spark MLib Modelling Big Data with Spark
  • Introduction to Spark GraphX
  • Understanding Data Science IOT data pipelines
  • Understanding MQTT and Kafka to build IOT systems
  • Configuring MQTT
  • Understand MQTT subscriber, publisher and brokers
  • Deploying HIVE MQTT services with IOT device
  • Understanding AWS IOT core
  • Configuring and managing AWS services
  • Deploy IoT devices with AWS IoT Core, AWS IoT Device
  • Management, and AWS IoT
    Analytics

What you’ll learn

  • Solve any data analytics problem using R and Python
  • Perform Logistic Regression using Cancer remission data set.
  • Linear regression in python
  • Data visualization in R-Bar graphs,Stacked bar,grouped bar graph,Line chart for time series data,Box plot to calculate mean, median, min ,max ,3rd quartile and 1st quartile values
  • Basics of Python,importing sys library,platform,checking the platform,run a code,checking data type,performing calculations,concatenating of strings,conversion of values,working on tuples,
  • Data mapping and indexing using enumerate,creating dictionaries using enumerate,sorting without order,reversing the list,append the list.
  • Mathematical on Array using Numpy,decalring the 2D array,3D array,dtype,numpy operations,boolean array
  • Vector operation with data alignment,creating data frame out of dictionary of equal length list,create Data frame by combining two series,create Data frame from array.
  • Perform Data exploratory functions,importing and exporting the data sets,Data manipulation
  • Data manipulation in R-Dply,Filter, multiple filter, mutate, arrange, summarize etc

Why should you choose a Career Data Science?

  • The number of Data Science and Analytics job listings is projected to grow by nearly 364,000 listings by 2020 - Forbes
  • The average salary for a Data Scientist is $120k as per Glassdoor
  • Businesses analysing data will see $430 billion in productivity benefits over their rivals not analysing data by 2020

Job Opportunities

  • Data Scientist/Analyst - Machine Learning/Deep Learning
  • Artificial Intelligence Expert - Data Scientist/ Machine Learning
  • Data Engineer
  • Research Engineer - Data Analytics & Artificial Intelligence

Key Features

  • Experiential Learning
  • Offline/Online Classes
  • Case studies and assignments
  • Hands on Projects
  • Mentoring Sessions
  • Job Assistance

Who all can Learn Data Science Course

SMEClabs Data Science course will help you to secure your future in data science. If you are someone who likes to analyze data then the Data science course from SMEClabs is the right choice for you. If you are someone who likes to handle challenging and complex situations then a career in data science is the right choice. Software engineers and analysts can who like to change their careers can also choose data science.
  • Freshers who want to start the career as we teach from the basics and gradually build up your skills.
  • Developers aspiring to be a ‘Data Scientist
  • Analytics Managers who are leading a team of analysts
  • Business Analysts who want to understand Machine Learning (ML) Techniques
  • Information Architects who want to gain expertise in Predictive Analytics
  • ‘R’ professionals who wish to work Big Data
  • Analysts wanting to understand Data Science methodologies
Data Science Course Master Advanced

Technologies Used Data Science

  • Python
  • Matplotlib
  • Seaborn
  • NumPy
  • Pandas
  • Flask
  • Django
  • Scikit-learn
  • Keras
  • TensorFlow
  • Natural Language Processing
  • MySQL
  • MongoDB
  • Web Scraping
  • Java
  • Hadoop
  • HBase
  • Hive
  • Scala
  • Apache Spark
  • Apache Kafka
  • AWS IOT
Data Science Course Master Advanced
Data Science Course Master Advanced
Data Science Course Master Advanced
Data Science Course Master Advanced
Data Science Course Master Advanced

FAQ - Frequently Asked Questions

  • Master in Data Science
  • Data scientist in Python
  • IBM Data science certification
  • Data science for business
Data scientist salaries in India would range from 2 to 20 lakh and the average salary of a Data scientist is 9 lakh.
Most organizations are using Data Science to carry out several organizational operations. Data is really important in each and every industry to take proper business decisions.
  • Deep learning
  • Mathematics
  • Machine learning
  • Programming
  • knowledge of Python
  • Data processing
  • Data analysis
AI can provide certain tools which will be able to automate certain operations in data science but it can’t replace data scientists.
  • Students feedback

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Data Science Course Master Advanced
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