Aug 16, 2023

Statistics is an integral part of data science and plays a crucial role in making sense of the vast amount of data generated in today's world. Whether you're a beginner looking to build a solid foundation or an intermediate learner seeking to enhance your statistical skills, Let's explore four remarkable courses that cater to learners at different levels and durations, offering invaluable insights into statistical concepts and their real-world applications.

**Level:** Beginner

**Duration: **Approx 14 hours

**Fee: **Free to audit

**What you’ll learn: **Exploratory Data Analysis, Descriptive Statistics, Sampling and Randomized Controlled Experiments Sampling, Probability, Sampling Distributions and the Central Limit Theorem, Regression, Common Tests of Significance, Resampling, Multiple Comparisons

**About the course: **In Stanford's "Introduction to Statistics," you'll learn how to think critically about data and make sense of it. By the end of the course, you'll be able to explore data, understand how to gather it properly, and use tests to find important patterns. These essential skills will set you up for further studies in statistical thinking and machine learning.

Link to the course:Here.

**Level: **Beginner

**Duration: **Approx 5-6 hours

**Fee: **Free to audit

**What you’ll learn: **Statistics, Linear Regression, R Programming, Regression Analysis, Rstudio, Exploratory Data Analysis, Statistical Inference, Statistical Hypothesis Testing

**About the course: **In this Specialization, you'll master analyzing and visualizing data using R. You'll create reports that others can reproduce, and learn about statistical inference to make data-driven decisions. Effectively communicate your findings without using complex statistical terms. You'll also be able to evaluate claims based on data and work with R packages to manipulate and present data visually.

Link to the course:Here.

**Level: **Intermediate

**Duration: **4 weeks (2-4 hours/week)

**Fee: **Free to audit

**What you’ll learn: **Random variables, Distributions, Inference: p-values and confidence intervals, Exploratory Data Analysis, Non-parametric statistics

**About the course: **In this course, you'll learn the R programming language specifically for statistical data and analysis in the life sciences.

You'll grasp the basics of statistical inference, understanding how to compute p-values and confidence intervals while using R code to analyze data.

Course also has ample clear examples that bridge the gap between concepts and practical implementation. To test your understanding and data analysis skills, you'll have problem sets with R programming exercises.

Through visualization techniques, you'll explore new data sets and choose the best approach for analysis. We'll also introduce robust statistical methods as alternatives when data deviate from standard assumptions. By utilizing R scripts for data analysis, you'll gain a solid foundation in reproducible research practices.

Link to the course:Here.

**Level: **Beginner

**Duration: **3 months (5 hours/week)

**Fee: **Free to audit

**What you’ll learn**: Python Programming, Data Visualization (DataViz), Statistical Model, Statistical inference methods

**About the course: **This Specialization consists of 3 courses namely:

- Understanding and Visualizing Data with Python,
- Inferential Statistical Analysis with Python,
- Fitting Statistical Models to Data with Python.

This specialization aims to teach you the basics and intermediate concepts of statistical analysis using Python. You'll discover where data comes from, learn about different data types, and explore data design and management. Effectively exploring and visualizing data will be a key skill you'll develop.

You'll also master using data for estimation and testing theories, creating confidence intervals, interpreting inferential results, and applying advanced statistical modeling techniques. By the end, you'll understand how to link research questions with the statistical and data analysis methods you've learned, making your analyses more insightful and valuable.

Link to the course:Here.

Statistics is the backbone of data science, enabling data scientists to extract meaningful insights, build predictive models, and make informed decisions from vast and complex datasets. Without a solid understanding of statistics, the practice of data science would lack the rigor and reliability needed to harness the true potential of data in solving real-world problems.

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