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Alexander Stewart
Alexander Stewart

Data Science For Business: What You Need To Kno... !!EXCLUSIVE!!


Statistical analysis and probability influence our lives on a daily basis. Statistics is used to predict the weather, restock retail shelves, estimate the condition of the economy, and much more. Used in a variety of professional fields, statistics has the power to derive valuable insights and solve complex problems in business, science, and society. Without hard science, decision making relies on emotions and gut reactions. Statistics and data override intuition, inform decisions, and minimize risk and uncertainty.




Data Science for Business: What you need to kno...



In data science, statistics is at the core of sophisticated machine learning algorithms, capturing and translating data patterns into actionable evidence. Data scientists use statistics to gather, review, analyze, and draw conclusions from data, as well as apply quantified mathematical models to appropriate variables. Data scientists work as programmers, researchers, business executives, and more. However, what all of these areas have in common is a basis of statistics. Thus, statistics in data science is as necessary as understanding programming languages.


Towards Data Science, a website which shares concepts, ideas, and codes, supports that data science knowledge is grouped into three main areas: computer science; statistics and mathematics; and business or field expertise. These areas separately result in a variety of careers, as displayed in the diagram below. Combining computer science and statistics without business knowledge enables professionals to perform an array of machine learning functions. Computer science and business expertise leads to software development skills. Mathematics and statistics (combined with business expertise) result in some of the most talented researchers. It is only with all three areas combined that data scientists can maximize their performance, interpret data, recommend innovative solutions, and create a mechanism to achieve improvements.


There are a number of statistical techniques that data scientists need to master. When just starting out, it is important to grasp a comprehensive understanding of these principles, as any holes in knowledge will result in compromised data or false conclusions.


While MOOCs in data science cannot replace the value of a comprehensive graduate program, they can help students refresh their knowledge on the basics. MOOCs are a cost-free option for data science professionals who need to brush up on statistics and mathematics skills. Current data science professionals benefit from online material by learning the latest trends and techniques, as the field is constantly changing. MOOCs are also useful for individuals who are on the fence about entering the field of data science. Especially when free, a MOOC is a low-risk method for testing out the field and seeing whether data science is worth pursuing.


With the growth of data science careers, educational institutions responded to the need for data skills by expanding vocational programs, such as data bootcamps. Data science bootcamps are compact, intensive educational programs that teach students the basic principles in data science. The goal for each bootcamp graduate is to be able to successfully interview for a data science role and ultimately be hired into the field.


The path toward a rewarding and challenging data science career begins with a strong graduate studies foundation, followed by the expansion of your machine learning portfolio and ongoing learning and sharpening of statistical literacy and programming skills.


Linear Models for Data Science: This course is an introduction to linear statistical models in the context of data science. Topics include simple and multiple linear regression, and generalized linear models. The primary software is R.


Practice and Application of Data Science I and II: This course covers the practice of data science, including communication, exploratory data analysis, and visualization. Also covered are the selection of algorithms to suit the problem to be solved, user needs, and data. Case studies will explore the impact of data science across different domains.


Scaling digital business especially complicates decision making and requires a mix of data science and more advanced techniques. The combination of predictive and prescriptive capabilities enables organizations to respond rapidly to changing requirements and constraints.


Analytics and BI platforms are developing data science capabilities, and new platforms are emerging in cases such as D&A governance. Cloud service providers are creating yet another form of complexity as they increasingly dominate the infrastructure platform on which all these services are used.


Traditional platforms across the data, analytics and AI markets struggle to accommodate the growing number of data and analytics use cases, so organizations must balance the high total cost of ownership of existing, on-premises solutions against the need for increased resources and emerging capabilities, such as natural language query, text mining, and analysis of semistructured and unstructured data.


Once widely implemented, data fabrics could significantly eliminate manual data integration tasks and augment (and, in some cases, completely automate) data integration design and delivery. However, data fabrics are still an emergent design concept, and no single vendor currently delivers, in an integrated manner, all the mature components that are needed to stitch together the data fabric. Ultimately, organizations must decide whether to develop their own data fabric using modernized capabilities spanning the above technologies and more, such as active metadata management.


This uses business intelligence (BI) tools, data visualization and dashboards to answer, what happened? or what is happening? Procurement, for example, can answer questions like, what did we spend on commodity X in the last quarter? and who are our biggest suppliers for commodity Y?


Combining predictive and prescriptive capabilities is often a key first step in solving business problems and driving smarter decisions. Understanding the potential use cases for different types of analytics is critical to identifying the roles and competencies, infrastructure and technologies that your organization will need to be truly data-driven, especially as the four core types of analytics converge with artificial intelligence (AI) augmentation.


Synthetic data, for example, is exploited by generating a sampling technique to real-world data or by creating simulation scenarios where models and processes interact to create completely new data not directly taken from the real world. This is most helpful with ML built on data sets that do not include exceptional conditions that business users know are possible, even if remotely. Such data is still needed to help train these ML models.


Data analysis is often considered the secondary component to data science. Data science is the foundation of big data that focuses on tools and methods, whereas data analytics is a focused approach to understanding the data and making it usable.


The data analyst is responsible for taking all the data and figuring out what it all means and then translating it into a format that is easy to understand and paints a picture of the story the data tells. 041b061a72


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