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Zero to Master Program

Data analytics Training

Data analytics training equips individuals with the skills and knowledge to effectively analyze and interpret data, deriving 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 applicable skills. Additionally, understanding ethical considerations and data privacy issues is crucial in data analytics training. Whether 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.

Fee

COURSE FEE

70,000

4.8

30K+ Learners enrolled

100 +

Duration(Hours)

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

Yes, this course is entirely offline, and we offer some courses online as well.

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

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Praveen Kumar

Founder & Instructor

Praveen has a full stack development experience and professional instructor and trainer for Flutter, Data Science, Machine Learning and Python Programming. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations.

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Lenin Prakash

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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