Statistics for Data science

📚 **Course Title:** Statistics for Data Science 🎓 **Headline:** Master the Mathematical Foundation of Data Science with Statistical Maths - From Basics to Advanced! --- **Unlock the World of Data Science with Solid Statistical Knowledge!** 🌍 Welcome to "Statistics for Data Science," the course that lays a robust mathematical foundation for your journey into the realm of data science. Here, we delve deep into the core of data science - **Statistical Maths**. This isn't just about crunching numbers; it's about understanding the language that speaks to data like none other. **Why Start with Statistics?** 🤔 Before diving headfirst into coding with Python or R, it's crucial to grasp the fundamental statistical concepts. My experience tells me that **Maths** is approximately 80% of data science, while programming accounts for the remaining 20%. By starting with statistics and applying these principles later with programming languages like Python and R, you'll have a more profound understanding and better application of data analysis techniques. **Course Structure:** 📈 **Lesson 1: Introduction to Data Science** - What is Data Science? - Chapter 1: Defining Data Science and its importance in the modern world. - Chapter 2: Basic statistical operations using Excel (Average, Mode, Min & Max). - Chapter 3: The multidisciplinary nature of data science. - Chapter 4: Two golden rules for applying maths in data science. 📈 **Lesson 2: Exploring Data** - Visualizing Data Spread - Chapter 4: Understanding the concept of spread and its significance. - Chapter 5: Mean, Median, Mode, Max, and Min. - Chapter 6: Identifying outliers, Quartiles, and Inter-Quartile Range (IQR). - Chapter 7: Comprehending Range and Spread. 📈 **Lesson 3: Diving into Probability and Distribution** - Standard Deviation, Normal Distribution, and the Empirical Rule - Chapter 8: Overcoming issues with Range and Spread calculations. - Chapter 9: Introduction to Standard Deviation. - Chapter 10: Understanding Normal Distribution and the Bell Curve. - Chapter 11: Real-world examples of Normal distribution. - Chapter 12: Plotting a bell curve in Excel. - Chapter 13: Exploring 1, 2, and 3 Standard Deviations. - Chapter 14: Learning the Empirical Rule - 68, 95, and 98%.75% In-depth. 📈 **Lesson 4: Z-Scores** - Probability and Its Application - Chapter 16: Calculating the probability of scores above/below a certain value (Z-Score). - Chapter 17: The likelihood of obtaining a specific value (e.g., 50%, 20%). - Chapter 18: Probability range for scoring between 40 to 60 (Z-Score). 📈 **Lesson 5: Binomial Distribution** - Understanding and Applying Probability Concepts - Chapter 22: Basics of binomial distribution. - Chapter 23: Calculating existing probabilities from historical data. - Chapter 24: Differentiating between exact and range probability. - Chapter 25: Practical Excel application of binomial distribution. - Chapter 26: Applying range probability in real-world scenarios. - Chapter 27: The rules governing the binomial distribution. **Why You Should Enroll:** - **Practical Excel Skills**: Learn to apply statistical concepts using practical Excel functions and techniques. - **Solid Foundation**: Establish a strong understanding of statistics which is essential for data science. - **Real-World Examples**: Discover how these statistical methods are used in everyday data analysis. - **Flexibility to Learn at Your Own Pace**: This course allows you to go through the material as quickly or slowly as you need, with a focus on mastering each topic before moving on to the next. **Who Is This Course For?** This course is designed for aspiring data scientists, current data science professionals looking to brush up on their statistical knowledge, and anyone interested in learning about the mathematical side of data science. Whether you're a beginner or looking to refine your skills, this comprehensive guide will equip you with the necessary tools and insights to navigate the world of data science confidently. Join us on this analytical adventure and transform the way you approach data science! 🚀📊✨

### **Overall Course Review** The online course "Excel for Data Science" received a global rating of **3.90**. Recent reviews from students paint a picture of a course that is both clear and helpful, particularly for beginners, but with some limitations in terms of content depth and complexity for more advanced data science students. #### **Pros:** - **Clear Instruction and Examples:** The instructor is commended for their clarity in discussing subject matter, providing relevant examples to illustrate discussions, and having a soothing and clear voice for instruction. - **Relevant Content for Beginners:** Many students found the course very helpful in understanding Excel's role in data science and gaining insights from data. The course was described as a good starting point for those new to the field or brushing up on their basic knowledge. - **Efficient Video Length:** The video content is praised for being appropriately long, allowing students to learn efficiently without being overwhelmed. - **Real-World Application:** A significant takeaway from the course is the understanding of how to derive insights from data using Excel, which is a practical skill applicable in various domains. - **Convenient for Beginners:** The use of Excel for exercises is considered very convenient and user-friendly, especially for beginners who are familiar with the software. #### **Cons:** - **Basic Content Limitations:** Some data science students found the course somewhat naïve, indicating that more in-depth lectures would have been beneficial. The content was seen as more akin to an Excel tutorial rather than a comprehensive data science course. - **Lack of Advanced Concepts:** A few reviews mentioned that concepts such as vectors, calculus, and hypothesis testing were not covered, which is particularly disappointing given the instructor's other offerings on these topics are freely available elsewhere. - **Desire for Additional Examples:** There is a suggestion that additional examples, particularly for mean, median, mode, quartiles, and interquartile range, would greatly enhance understanding of these statistical concepts within the context of data science. #### **Course Refresh and Foundation:** The course serves as a good refresher for those who have become accustomed to more complex theories and applications but need to revisit basic math and statistics principles. It is also suitable for beginners looking to establish a foundation in data science with an emphasis on Excel. #### **Final Verdict:** For those starting their journey into data science or looking to solidify their understanding of the basics, "Excel for Data Science" is a valuable resource. However, students interested in more advanced data science applications may find this course limited and might prefer to supplement it with additional resources that cover higher-level concepts like vectors and calculus. Overall, the course is recommended as a starting point, with the caveat that more experienced learners might seek out more comprehensive courses to further their data science knowledge.