Knowledge of statistics enables data scientists to improve their workflows by using mathematical concepts to refine processes such as data analysis.
Statistics is essential for the data science career¹ and students can advance their skills by using the best statistics resources.
Whether you are a beginner or experienced in data science, statistics is vital as you navigate the data science career.
Most students think statistics is hard. On the contrary, it is simple because you need to connect ideas and practice problem solving.
Resources from top experts in data science and accredited institutions such as MIT and Stanford offer the best learning tools in statistics and mathematics².
Are you ready to learn statistics?
In this article, I will explore top statistics resources to help you become better in data science. In the end, I will offer insights on transitioning to the data science field and planning your schedule to improve learning productivity. Stay tuned.
Top Statistics Resources for Data Science
The following resources can help you today become better in data science:
1. The Open University Open Learn Resource
The Open University applies to those with no coding experience and offers a good starting point when learning statistics. Students using the Open University database benefit from a wide range of resources from accredited institutions including instructor support.
The field of data science³ is changing and the statistical resources from Open University facilitate learning in a structured manner.
Data interpretation lessons from the Open University will support your journey in learning statistics by using practical examples and solutions.
Statistics require definition of problems and the Open University enables students to learn statistics by using case examples where they interpret variables. Boxplots from the Open University are one area of data interpretation students use in their learning process.
Tables and graphs make learning statistics⁴ better and the Open University offers these by explaining statistical concepts. By using charts to interpret data, students use the Open University to facilitate their data science journey.
2. Statistics at Square One
This book authored by TDV Swinscow supports students of data science and explores samples, standard deviation, and confidence intervals.
Unlike many statistical books with complicated explanations, the author creates an integrated learning model that enables students to grasp challenging statistical concepts.
You cannot learn statistics without paired alternatives and percentages. This book explores these statistical concepts with the needs of students in mind. The author discusses statistical errors including type I and types II that students of data science can use to detect anomalies⁵ in their data sets.
Standard deviation comes next with the author explaining chi-squared tests alongside the t-tests.
Correlation and regression explanations in this book are essential for students of data science as they navigate statistics to apply in their workflows. The explanations on regressions in statistics make this book appealing to new students of data science.
3. MIT Open Course Ware
The MIT OpenCourse Ware offers statistical resources from materials collected from MIT. As an institution with an international reputation, MIT offers statistical courses that students learning #statistics need in their course requirements.
If you want the best learning resources for statistics, then MIT OpenCourse Ware is for you because of their practical lessons.
With over 2000 learning materials online, the MIT OpenCourse Ware offers a great platform for mastering statistical skills. With tutors experienced in diverse fields, you can access the brains of brilliant people in the field of statistics. Online classes offered at the MIT OpenCourse Ware facilitate student needs as they learn statistics applicable in data science.
The hands-on approach of teaching students at MIT OpenCourse Ware makes this learning resource-effective for students interested in statistics for data science. Students interact with tutors, take assignments, and learn from feedback provided online. Students have the option of downloading videos for courses, which they feel, need more attention.
4. Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression
Introduction to ANOVA applies to students looking to learn statistical analysis concepts including logistic and linear regression. For those interested in SAS, then this resource suits you because of the various course materials offered.
The SAS Certified Trials Programmer Using SAS-9 and SAS Statistical Business Analysis Using SAS 9 are some certifications students acquire from using this resource.
Students learn about sample statistics by applying univariate procedures, inferential, and descriptive statistics as well. You can become a skilled expert in statistics by using Statistics 1 because of practical lessons on data distributions and sample statistics interpretation.
The topic of confidence intervals taught in Statistics 1 enables #datascience students to construct their models with guidance. Beginners of statistics find this resource useful because of learning basics in the introduction phase.
5. Probability and Statistics in Data Science using Python
Probability and Statistics in Data Science using Python is offered by edX and suits students learning statistics for data science because of support from professional tutors.
#Machinelearning basics taught to students from this course enables them to grasp concepts for application in their projects. At the same time, mathematics courses offered in this resource support students in implementing ideas for application in the real world.
Statistical confidence levels are essential and students using this resource learn these concepts by consulting with instructors. The tutor-student interaction on this online course makes it an ideal tool for learning statistics. Students interact by asking questions and using feedback to improve their learning.
Jupyter notebooks taught to students on this course offers them skilled experience for handling problems based on mathematical concepts. Course areas including MDL, PCA, and correlation makes this platform suitable for those interested in upgrading their statistical skills. Students can also learn dependence and random variables.
6. Statistical Concepts Explained in Simple English
The Statistical Concepts Explained in Simple English offers is another great resource for students of data science. From regression, data reduction, and cross-validation, this resource provides topics and assignments students need to ace everything related to statistics.
The experimental design is another area students learn in addition to statistical analysis.
Besides statistics, students can learn Python and #neuralnetworks as used in the data science field. The combination of different skills promotes effective learning as students advance their careers.
The moderators in this resource support students in assignments and their collaboration tools make learning a great experience. Students learn more than 20 statistical concepts and have options for pursuing their interest areas.
7. The Open Source Data Science Masters
Statistical analysis matters for students as they reskill and transition in their careers. The Open Source Data Science Masters⁷ is the best fit.
Students interact with instructors on statistical subjects of interest and use practice examples to facilitate understanding.
Computing lessons offered on The Open Source Data Science Masters support students in the development of new skills alongside mathematical concepts as applied in statistics.
The easy navigation of statistics resources means that students can explore the information and this makes learning smooth. Data design courses offered in The Open Source Data Science Masters exposes students to concepts and case examples.
KDnuggets is one of the most popular online resources you can learn statistics and data science. Ranked as one of the best platforms for statistics, you should try KDuggets because of the many tools offered to students. The simple and clear explanations from KDnuggets offers students an ideal platform for accessing statistical resources.
The use of graphs and models for statistics on KDnuggets improves the learning experience of students. From collaboration tools such as question-answer options from top experts, students can use KDnuggets to facilitate their learning needs. Beginners and those experienced in data science will find KDnuggets an important resource in their learning experiences.
9. The OpenIntro Statistics
Those learning statistics for the first time will find this book interesting because of the systematic explanations on foundations of statistics. Authors David Diez and Christopher Barr introduces students to data, probability and distribution of random variables. Categorical and numerical data topics covered guide learners as they explore statistics with more lessons on linear regression.
The book explores the topic of logistic regression and multiple regression. I admire this book because of the simple language the authors use to introduce statistics by explaining the relationships between variables and interpreting results.
10. Harvard University Statistical Inference and Modeling
Offered by Harvard University, this course enables students to learn various statistics topics⁶ including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory #dataanalysis.
The course introduces statistical modeling and how it is applied to high-throughput data. The course explores parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation.
Students can expect lessons on hierarchical models and empirical bayes along with some examples of how these are used in practice. Additionally, students learn R #programming examples to make the connection between concepts and execution.
You can become better at Data Science in 2020
As an experienced Data Scientist at Galvanize⁸, here is a tip I advice you should take note of:
Start taking courses in steps and completing them without pushing yourself too much. For instance, statistical courses need patience and consultations as you take them.
Secondly, use many resources as possible to increase your awareness about the latest trends in the industry. Do not focus on courses that will hold you back and prevent you from reaching your goals.
Thirdly, research the best free online resources you can use to facilitate your learning of statistics.
All the best as you learn to become better at data science.