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 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:

Syllabus for Data Science Course - Master Level

Learn Python Program from Scratch

Course Introduction
Programming

Statistical and Mathematical Essential for Data Science

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

Data Science with Python

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

Machine Learning

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

AI (Natural Language Processing)

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

Flask Web Development

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

Django Web Development

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.

Big Data Analytics

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

AI, Deep Learning, Computer vision (OpenCV)

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

Kafka, MQTT and AWS IOT

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?

Job Opportunities

Key Features

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

Technologies Used Data Science

FAQ - Frequently Asked Questions

Which course is better for a career in Data science?

Master in Data Science
Data scientist in Python
IBM Data science certification
Data science for business

Is data science a good paying job?

Data scientist salaries in India would range from 2 to 20 lakh and the average salary of a Data scientist is 9 lakh.

Is Data science a good career choice in 2023?

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.

What are the major skills required for the Data scientist?

Deep learning
Mathematics
Machine learning
Programming
knowledge of Python
Data processing
Data analysis

Is AI a threat to Data science?

AI can provide certain tools which will be able to automate certain operations in data science but it can’t replace data scientists.

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