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What are the best ways to teach yourself statistical programming?

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Statistical programming is a valuable skill that can help you analyze data, create visualizations, and build predictive models. Whether you want to pursue a career in data science, research, or business, learning how to code with statistics can give you an edge over the competition. But how can you teach yourself statistical programming without spending a fortune on courses or books? Here are some of the best ways to learn this skill on your own.

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Uzzal Saha

Statistical Programmer at Shafi Consultancy
Awais Kamran

Engineering Lead @ Conrad Labs | AWS Certified Solution Architect | Full Stack Developer | Machine Learning Enthusiast…

1 Choose a language

The first step to teach yourself statistical programming is to choose a language that suits your goals and preferences. There are many options available, such as R, Python, SAS, Stata, and MATLAB, each with its own strengths and weaknesses. For example, R and Python are popular among data scientists and researchers, as they have a large number of libraries and packages for data analysis, visualization, and machine learning. SAS and Stata are widely used in the industry and academia, as they offer robust and reliable tools for statistical modeling and inference. MATLAB is a powerful language for numerical computing and engineering applications. You should research the pros and cons of each language, and pick one that aligns with your interests and needs.

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Uzzal Saha

Statistical Programmer at Shafi Consultancy
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Some ways to get started, learn: - why Data Analysis is important - what type of data analysis is exactly used in the everyday world - what is Programming - what is Statistical Programming - Python, R, SAS, Stata, SPSS, Matlab etc.

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2 Find online resources

The next step to teach yourself statistical programming is to find online resources that can help you learn the basics and advance your skills. There are many free or low-cost resources available, such as tutorials, videos, blogs, podcasts, books, and courses. Some of the most popular and reputable ones are:

- Codecademy : A platform that offers interactive and gamified lessons on various programming languages, including R and Python.

- Coursera : A platform that offers online courses from top universities and organizations, covering topics such as data analysis, statistics, and machine learning. You can audit the courses for free or pay for a certificate.

- Khan Academy : A platform that offers free videos and exercises on math, science, and computing, including statistics and probability.

- DataCamp : A platform that offers online courses and projects on data science and analytics, using R, Python, SQL, and more.

- Stack Overflow : A platform that offers a community of experts and learners who can answer your questions and help you solve your problems.

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Awais Kamran

Engineering Lead @ Conrad Labs | AWS Certified Solution Architect | Full Stack Developer | Machine Learning Enthusiast | Tech Speaker
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There are various platforms and websites, such as Coursera, edX, and Khan Academy, that offer courses and tutorials covering a wide range of statistical programming languages like R and Python. YouTube, with its vast library of video tutorials, is another valuable resource for visual learners. Additionally, websites like Stack Overflow and GitHub provide a community-driven environment for seeking answers to specific questions and exploring open-source statistical programming projects. Online forums and discussion boards dedicated to data science and statistics, such as Cross Validated and Reddit's r/statistics, offer valuable insights and solutions to common programming challenges.

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3 Practice with real data

The third step to teach yourself statistical programming is to practice with real data and apply what you have learned. You can find data sets from various sources, such as Kaggle, UCI Machine Learning Repository, Google Dataset Search, and more. You can also use your own data or create synthetic data. You should try to explore the data, perform descriptive and inferential statistics, create visualizations, and build predictive models. You should also document your code, comment your logic, and share your results.

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Awais Kamran

Engineering Lead @ Conrad Labs | AWS Certified Solution Architect | Full Stack Developer | Machine Learning Enthusiast | Tech Speaker
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Practicing statistical programming with real data is a highly effective way to solidify your skills. Begin by sourcing real datasets from reputable sources like government databases, research publications, or open data platforms. Then, import the data into your chosen statistical programming environment, such as R or Python, and explore it by conducting various analyses, creating visualizations, and running statistical tests. You can also participate in data analysis challenges on platforms like Kaggle to tackle real-world problems and learn from others' approaches. The key is to engage with actual data, as it exposes you to the complexities and nuances of real-world scenarios, helping you become a more proficient statistical programmer.

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4 Join online communities

The fourth step to teach yourself statistical programming is to join online communities that can offer you support, feedback, and inspiration. You can connect with other learners and experts who share your passion and curiosity for statistics and coding. You can also participate in challenges, competitions, and hackathons that can test your skills and expose you to new problems and solutions. Some of the most popular and active online communities are:

- r/statistics and r/learnprogramming : Subreddits that offer discussions, resources, and advice on statistics and programming.

- Data Science Central : A website that offers blogs, webinars, podcasts, and events on data science and analytics.

- Kaggle : A website that offers data sets, competitions, notebooks, and forums on data science and machine learning.

- Meetup : A website that offers local and online events and groups on various topics, including statistics and programming.

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5 Keep learning and improving

The fifth step to teach yourself statistical programming is to keep learning and improving your skills and knowledge. You should always be curious and willing to learn new things, as the field of statistics and programming is constantly evolving and expanding. You should also review your code, identify your mistakes, and seek feedback from others. You should also track your progress, celebrate your achievements, and set new goals. You should also explore other topics and domains that can enhance your statistical programming skills, such as:

- Linear algebra : A branch of mathematics that deals with vectors, matrices, and systems of linear equations, and is essential for understanding and implementing many algorithms and models.

- Calculus : A branch of mathematics that deals with functions, limits, derivatives, and integrals, and is useful for optimizing and analyzing functions and models.

- Machine learning : A branch of artificial intelligence that deals with creating and training algorithms and models that can learn from data and make predictions or decisions.

- Data visualization : A branch of data science that deals with creating and communicating graphical representations of data and information.

By following these steps, you can teach yourself statistical programming and become a proficient and confident coder. You can also open up new opportunities and possibilities for your career and personal growth.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the best ways to teach yourself statistical programming?

1

2

3

4

5

6

1 Choose a language

2 Find online resources

3 Practice with real data

4 Join online communities

5 Keep learning and improving

6 Here’s what else to consider

Programming

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What are the best ways to teach yourself statistical programming?

1

2

3

4

5

6

1 Choose a language

2 Find online resources

3 Practice with real data

4 Join online communities

5 Keep learning and improving

6 Here’s what else to consider

Programming

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