Create Real World Data Science Project Web Applications [Free Online Course] - TechCracked

Learn to create Data Science Web application projects with python and deploy on heruko

What you'll learn

• Build Data Science Models
• Deployment Of Data Science Applications
• Data Science Practical Applications
• Why They Need To Learn Data Science

Description

Data science is centered on building, cleaning, and organizing datasets. Data scientists create and leverage algorithms, statistical models, and their own custom analyses to collect and shape raw data into something that can be more easily understood.

Data scientists lay the groundwork for all of the analyses an organization performs. They do this by performing key functions, including:

• Data wrangling: The process of cleaning and organizing data to be more readily used
• Statistical modeling: The process of running data through different models—such as regression, classification, and clustering models, among others—to identify relationships between variables and gain insight from the numbers.
• Programming: The process of writing computer programs and algorithms in a variety of languages—such as R, Python, and SQL—that can be used to analyze large datasets more efficiently than through manual analysis.

While it’s unlikely you’ll need to perform any of these duties in your job unless you’re specifically hired as a data scientist, data science still holds value for business professionals. Familiarizing yourself with the concepts, terminology, and techniques used by data scientists on your team can empower you to better communicate with these important professionals and give you a firmer understanding of what insights are and aren’t possible to glean from the data.

Additionally, a proficiency in key data science skills can enable you to assess and draw insights from your organization’s data—increasing the value you bring to your organization while reducing your reliance on others. Developing your data science skills can allow you to:

• Identify and avoid mistakes that commonly arise while interpreting datasets, metrics, and visualizations
• Embrace data-driven decision-making and ensure your business decisions are backed by numbers
• Form hypotheses, run experiments, and gather evidence that empowers you to recognize business challenges and solutions
• Understand market size, buyer trends, competition, and opportunities and risks your business faces

Also See : Build Data Science & Machine Learning Projects From Scratch

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