Course Includes
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Recorded Lessons: 14
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Recorded Hours: 5
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Duration:
1 month 1 day (Avg)
Course Features
Access on mobile
TDP Assessment Test
Top Skills Covered
Overview
Course Description
This comprehensive program equips students with the expertise to derive meaningful insights from data, employing advanced statistical and machine learning techniques alongside adept data visualization and operational skills. Throughout the course, students master popular data analysis tools, such as Python and SQL delve into cutting-edge realms like natural language processing (NLP) and deep learning. Engaging in hands-on projects, they apply their knowledge to solve real-world challenges, cultivating a skill set tailored for making informed decisions grounded in data across diverse industries. The curriculum spans the entirety of data science, encompassing machine learning operations and proficiency in NLP and deep learning with practical experience in real-time industry projects. Additionally, participants gain mastery in visualization tools such as Tableau and PowerBI, further enhancing their capabilities in presenting and interpreting data-driven insights.
1.Interdisciplinary Expertise
Strength your foundational Knowledge apply this understanding to specific problems, and build competencies who work in multiple Programming environments.
2.Strong Academic Support
Learn From a variety of Activities and through high degree of Communication Between Faculty and Students
3.Self Placed Learning
Engage and learn on your own faq Explore our extensive, access your learning materials online anytime, anywhere attend live c, and record lectures and talks that work for your schedule.
4.Industry Oriended Ourriculum
Gain real-world insights from industry-focused module projects and learn from faculty who come with Decodes of rich industry Experience & Expertise.
- Qualifed Faculty & Research
- State of the Art-Infrastructure
- Data sets and Database
- Professional Development Oppurtunities
- Carrier placement support
- Continue improvement Feedback
- Ethical Consideration & Responsible AI
Data Science
Modulation digital provides the best Data Science Coaching in Laxmi Nagar Delhi these courses are open to all graduates post graduation and working professionals
- Topics Covered:
Introduction to Python
Python for Data Science
Data Visualisation in Python
Data Analysis Using SQL (Optional)
Advanced SQL and Best Practices (Optional)
Data Analysis in Excel
Analytics Problem Solving
Math for Machine Learning
- Exploratory Data Analysis
Cloud Essentials: Intro to Git & Github
Inferential Statistics
Hypothesis Testing
Lending Club Case Study
What you'll learn
- Comprehensive Understanding: Develop a thorough understanding of the core concepts, principles, and techniques of digital marketing, including its various channels, strategies, and tools. Website Optimization: Acquire the skills to design, develop, and optimize effective websites that enhance user experience (UX), drive traffic, and support overall digital marketing objectives. Career Readiness: Equip yourself with the skills, knowledge, and confidence needed to pursue a career in digital marketing or advance your existing marketing career to new heights. Practical Experience: Engage in hands-on exercises, real-world projects, and case studies that simulate the challenges and scenarios encountered in the digital marketing industry. Strategic Planning and Execution: Learn how to develop comprehensive digital marketing strategies, set SMART goals, define target audiences, allocate resources effectively, and implement campaigns that align with business objectives. Search Engine Marketing (SEM) Skills: Acquire pr
Requirements
- Basic Computer Skills
- Strong Math Skills. Most data science professionals already have a strong background in mathematics, either from high school, college, or both. ...
- Familiarity with Object-Oriented Programming (OOP) ...
Course Content
13 Lessons | 5:00 Total hours
Data Analytics
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Introduction Of Python 00:25 -
Python Objects 00:33:08
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Python Operators 00:22:38
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Python Functions
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Pandas 00:20:41
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Numpy 00:36:00
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Data Frame Manipulation 00:21:45
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Visualiation 00:07:58
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Intro to Statistics 00:36:07
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Probability Theory 00:21:03
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Probability Distribution 00:15:55
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Hypothesis Testing 00:32:27
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ANOVA and Chi-Square 00:26:44
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Data analytics
Frequently asked questions
How to become Data analysts
data analysts gather, clean, and study data to help guide business decisions. If you’re considering a career in this in-demand field, here's one path to getting started. Get a foundational education. Build your technical skills. Work on projects with real data. Develop a portfolio of your work. Practice presenting your findings. Get an entry-level data analyst job. Consider certification or an advanced degree.
What topics will be covered in this course?
This course covers a range of topics including data exploration, statistical analysis, machine learning algorithms, data visualization, big data technologies, and Hypothesis Testing and many more..
How will this course help in advancing my career in data science and analytics?
By mastering the concepts and tools covered in this course, you will be well-prepared for roles such as data analyst, data scientist, business analyst, or machine learning engineer in various industries.
What topics will be covered in this course?
The course will cover essential topics such as data collection and cleaning, exploratory data analysis, statistical methods, machine learning algorithms, data visualization techniques, and practical applications in real-world scenarios.
What programming languages and tools will I learn?
You will primarily learn Python and R for data analysis and machine learning. Additionally, you may work with tools like Jupyter Notebook, Pandas, NumPy, Matplotlib, Seaborn, and SQL for data manipulation and visualization.