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Zero to Master Program

Data analytics Training

Data analytics training equips individuals with the skills and knowledge needed to effectively analyze and interpret data to derive meaningful insights. In today's data-driven world, organizations rely on data analytics professionals to make informed decisions, optimize processes, and gain a competitive edge. Training programs typically cover a range of topics, including statistical analysis, data visualization, machine learning, and programming languages such as Python or R. Hands-on experience with real-world data sets and practical projects are often incorporated to provide learners with practical skills. Additionally, understanding ethical considerations and data privacy issues is crucial in data analytics training. Whether it's through online courses, workshops, or formal education programs, investing in data analytics training can open up diverse career opportunities in industries such as finance, healthcare, marketing, and beyond.

4.8

30K+ Learners enrolled

60+

Hours of lectures

350+

Problems

This is where you embark on an amazing journey!

Most flexible program in the industry

Freedom to learn

Watch classes any time at your convenience

Cheat days

Catch up on the course when life is calling you elsewhere

Features that keep you going

A structured curriculum that makes learning easy

Practice code problems of varying difficulty

Engagement coach to keep you motivated

Compile & run in an integrated coding environment

Get doubts resolved in 30 mins

1:1 sessions over voice call & chat with our skilled teaching assistants

Industry leading mentors to help you grow

1:1 Mock interviews with resume and career guidance

Structured feedback to make you better

Get a chance to be referred to your mentors’ company

Experience a seamless job switch with hiring assistance

Skill-based hiring across all levels of experience

The results

110%

Average salary hike for Data Analytics

7000+

Transitions to product companies

250+

Trusted placement partners

Frequently asked but seldom read questions

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

None! Whether you’re completely new to the job field or have had some exposure, Algoxfusion Certificate is the right program for you..

You can find the ‘Get Certified’ option at the end of the Lessons in the table of contents. it will be shown only after completing all the lessons with 100% progress.

Submissions are evaluated based on criteria such as correctness, completeness, critical thinking, and adherence to instructions, typically using rubrics or grading guidelines provided by the instructor.
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Meet the faculty legends that will make you legendary

Praveen Kumar

Founder & Instructor

He holds a Bachelor’s degree in Computer Science from India’s most premier institute - IIT Delhi and a Master’s degree in Computer Science from Stanford University. He is a coding enthusiast and has worked with bigwigs like Amazon and Facebook in the past.

Karthiga

Co-Founder & Instructor

He is an expert in JavaScript & React (Front-end) and has worked on open-source projects like Firebug and Zulip. He has also served as a GCI (Google Code-In) Mentor with Zulip. In his previous role as a Software Engineer he has worked for Goibibo-MMT.

Course curriculum for the curious

Overview of Data Analytics
Supervised vs. Unsupervised Learning
Classification vs. Regression
Introduction to Algorithms: Decision Trees, Random Forest, K-Nearest Neighbors
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Introduction to Data Visualization
Importance of Data Visualization in Data Analytics
Tools for Data Visualization (Excel, Tableau, Power BI)
Basic Charts and Graphs (Bar charts, Line charts, Pie charts)
Best Practices in Data Visualization
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Introduction to Data Preprocessing
Data Cleaning Techniques
Handling Missing Values
Removing Duplicates
Data Transformation: Normalization and Standardization
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Descriptive Statistics: Mean, Median, Mode
Measures of Variability: Range, Variance, Standard Deviation
Inferential Statistics: Hypothesis Testing, Confidence Intervals
Correlation and Regression Analysis
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Overview of Machine Learning
Supervised vs. Unsupervised Learning
Classification vs. Regression
Introduction to Algorithms: Decision Trees, Random Forest, K-Nearest Neighbors
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Introduction to Python for Data Analytics
Basics of Python Programming
Data Analysis Libraries: NumPy, Pandas
Data Visualization with Matplotlib and Seaborn
Hands-on exercises and projects integrating concepts learned throughout the course
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Students work on a capstone project applying the concepts learned throughout the course
Guidance and support provided by instructors
Presentation of capstone projects by students
Data Visualization with Matplotlib and Seaborn
Hands-on exercises and projects integrating concepts learned throughout the course
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