Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
17 Hour(s) 0 Minute(s)
Language
English
Taught by
Manifold AI Learning ®
Rating
Course Overview
Statistics you need at the Project : Descriptive and Inferential statistics, Hypothesis testing, Regression analysis
Are you interested in pursuing a career as a Marketing Analyst, Business Intelligence Analyst, Data Analyst, or Data Scientist, and are eager to develop the essential quantitative skills required for these roles? Look no further!
Enter the world of Statistics for Data Science and Business Analysis – a comprehensive course designed to be your perfect starting point. With included Excel templates, this course ensures you quickly grasp fundamental skills applicable to complex statistical analyses in real-world scenarios. Here's what sets our course apart:
Easy to comprehend
Comprehensive
Practical
Direct and to the point
Abundant exercises and resources
Data-driven
Introduces statistical scientific terminology
Covers data visualization
Explores the main pillars of quantitative research
While numerous online resources touch upon these topics, finding a structured program explaining the rationale behind frequently used statistical tests can be challenging. Our course offers more than just automation; it cultivates critical thinking skills. As an aspiring data scientist or BI analyst, you'll learn to navigate and direct computers and programming languages effectively.
What distinguishes our Statistics course?
High-quality production with HD videos and animations
Knowledgeable instructor with international competition experience in mathematics and statistics
Comprehensive training covering major statistical topics
In-depth Case Studies to reinforce your learning
Excellent support with responses within 1 business day
Dynamic pacing to make the most of your time
Why acquire these skills?
Salary/Income boost in the flourishing field of data science
Increased chances of promotions by supporting business ideas with quantitative evidence
A secure future in a growing field that's automating jobs rather than being automated
Continuous personal and professional growth with daily challenges and learning opportunities
Remember, the course is backed by Udemy’s 30-day unconditional money-back guarantee. Take the plunge – click 'Buy now' and embark on your learning journey today!
Course Content
- 8 section(s)
- 137 lecture(s)
- Section 1 Foundation of Statistics
- Section 2 Exploratory Data Analysis
- Section 3 Probability
- Section 4 Inferential Statistics
- Section 5 Section 5 - Linear Regression
- Section 6 Section 6 - Logistic Regression
- Section 7 Section 7 - Miscellaneous Stats Concepts in Machine Learning Areas
- Section 8 Machine Learning for Projects
What You’ll Learn
- Learn Underlying Mathematics to build an intuitive understanding & relating it to Machine Learning and Data Science
- Hands-On Code Implementation with Python for each mathematical topic to deepen the knowledge
- Master the Advanced level in an Interactive learning approach to Strengthen your knowledge on Difficult & Important Topics
- Understand the Importance of Probability & Distributions, and choose the right function for your data.
Skills covered in this course
Reviews
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JJoão Pedro de Alencar Costa
The course order was misleading at some point. For the machine learning session, the codes were just added without any previous explanation or context. In the end, these last bonus classes shared interesting topics, but without introduction as well, so it was difficult to understand. From my point of view, an extra effort on theory explanation for all sessions looking for beginners would increase this course score by much. Moreover, it fits my purpose of applying and performing Data Science along Python and ML. Thus, I am very thankful!
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SSean Cleary
This course is so far (lecture 32) the most disorganized mess I've ever tried to struggle through. The instructor uses undefined terms that, if we're lucky, he may define three lectures later. Skewness and kurtosis for example - neither has been defined. I know that they are, like variance, expected values of (x - mu)^n, where n=2, 3, & 4 for Var, Skew, & Kurt. All he's said was that the Fisher & Pearson kurt differ by three, but not why (for comparison to the standard normal distribution, which has kurt = 3 - you're welcome). He doesn't seem to think we need to know what the metrics we calculate actually are. It's like we're not being taught, but trained, but he doesn't explain the training, either - what various functions' arguments do. I am baffled by the course's high ratings when I am learning almost nothing and barely learning to do anything, either.
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WWuppukonduru Venkata Vivek
Nachiketh murthy is the best training for those looking to learn AI ML . He deserves a standing ovation for the work he has done to make these courses. Continue the great work
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LLieke Meussen
Zeer onduidelijk waar relevante data files te vinden. Heel veel irritante fouten in hands on code.
