Diploma in Data Science
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Intermediate Level
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English
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Last updated 01/2025
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Instructed by Sanak Sundar
Enhancing data science skills leads to a rewarding and impactful career.
What you'll learn
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Data science is a dynamic field that follows technological advancements.
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Data scientists benefit from high-paying salaries due to their specialized skills.
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Beyond data scientist roles, data science opens doors to various job paths.
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Data science allows you to make a difference by solving real-world problems using data-driven insights.
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Gain proficiency in programming languages, statistics, and math.
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Effective communication is crucial for sharing insights gathered from data.
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Data science skills are in high demand across industries, ensuring long-term career stability.
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As data science evolves, your skills remain valuable and adaptable.
Course content
Overview of Data Science
Importance of Data Science in today’s world
Understanding databases (SQL, NoSQL)
Working with big data technologies (Hadoop, Spark)
Basics of computer networks
Data transfer protocols
Importance of data privacy and ethics
Implementing data encryption and secure data transfer
Introduction to DevOps
Implementing CI/CD pipelines for data science projects
Developing data-driven web applications
Working with APIs to fetch and send data
Exploratory Data Analysis
Statistical analysis and hypothesis testing
Predictive modeling and machine learning
Implementing a real-world solution using the skills acquired throughout the course
Get a completion certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Course Overview
Module 1: Overview of Data Science
- Understanding the field of data science
- The role and responsibilities of a data scientist
- Importance of data science in various industries
- Case studies of successful data science projects
Module 2: Data Infrastructure
- Introduction to databases
- Understanding SQL databases:
MySQL, PostgreSQL
- Understanding NoSQL databases:
MongoDB, Cassandra
- Big data technologies
- Introduction to Hadoop: HDFS,
MapReduce
- Introduction to Spark: RDDs,
DataFrames, Spark SQL
Module 3: Data Networking
- Understanding the basics of computer networks:
LAN, WAN, protocols
- Data transfer protocols: HTTP, FTP, SFTP
- Network security and firewalls
Module 4: Data Security
- Importance of data privacy and ethics in data
science
- Understanding data encryption: symmetric,
asymmetric, hash functions
- Secure data transfer: SSL, TLS, HTTPS
Module 5: DevOps for Data Science
- Introduction to DevOps: principles and practices
- Understanding CI/CD pipelines: Jenkins, Travis CI
- Containerization and virtualization: Docker,
Kubernetes
Module 6: Data Applications
- Developing data-driven web applications: Flask,
Django
- Working with APIs: REST, SOAP
- Data visualization in web applications: D3.js,
Chart.js
Module 7: Data Analytics
- Exploratory Data Analysis: pandas, matplotlib,
seaborn
- Statistical analysis and hypothesis testing:
t-test, chi-square test, ANOVA
- Predictive modeling and machine learning:
regression, classification, clustering
Module 8: Capstone Project
- Identifying a real-world problem that can be
solved using data science
- Gathering and cleaning data
- Performing exploratory data analysis
- Building and evaluating a predictive model
- Presenting the results in a clear and
understandable manner
Recommended Courses
Next batch starts on 15th Feb
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