Enroll Course: https://www.coursera.org/learn/statistics-and-data-analysis-with-excel-part-1
Embarking on a journey into data science, data analytics, or machine learning often begins with a solid understanding of statistics. For those new to the field, Coursera’s ‘Statistics and Data Analysis with Excel, Part 1’ is an excellent starting point. This course is meticulously designed for beginners, assuming no prior knowledge of statistical concepts. It provides a robust foundation that seamlessly prepares you for more advanced topics and subsequent courses, including its direct follow-up, ‘Statistics and Data Analysis with Excel, Part 2,’ and the R-focused ‘Statistics and Data Analysis with R.’
The syllabus is thoughtfully structured to guide learners through essential statistical concepts using Microsoft Excel, a tool many are already familiar with. The course kicks off with an orientation to course policies and fundamental statistical ideas.
Week 2 delves into Descriptive Statistics and Graphical Representation of Data. Here, you’ll learn to calculate key statistics like population and sample means, quartiles, and percentiles. Crucially, the course emphasizes data visualization, teaching you to create histograms, scatter plots, and column plots. Box plots are also covered, offering a valuable technique for identifying outliers. The module also touches upon data cleaning, transformation, and the use of robust estimators for datasets significantly impacted by outliers.
Probability is the focus of Week 3. This section is vital, as a firm grasp of probability is fundamental to all statistical study. You’ll explore the rules and axioms governing probability through screencasts and gain a deep understanding of conditional probability, which serves as the bedrock for Bayes’ Theorem.
Weeks 4 and 5 tackle Probability Distributions. Week 4 concentrates on Discrete Probability Distributions, where the random variable is restricted to discrete values. You’ll learn about distributions like the binomial, geometric, negative binomial, hypergeometric, multinomial, and Poisson distributions, which are essential for making probabilistic predictions in discrete stochastic models.
Building upon this, Week 5 explores Continuous Probability Distributions. This module covers the widely used normal and standard normal distributions, as well as the exponential and gamma distributions, among others. These are critical for making probabilistic predictions in continuous stochastic models.
What makes this course particularly recommendable is its practical approach. By using Microsoft Excel, the course makes abstract statistical concepts tangible and applicable to real-world data. The assignments are designed to reinforce learning through hands-on practice. For anyone looking to build a foundational understanding of statistics for data-related careers, ‘Statistics and Data Analysis with Excel, Part 1’ is a highly recommended and accessible starting point.
Enroll Course: https://www.coursera.org/learn/statistics-and-data-analysis-with-excel-part-1