what does data scientist do

“More generally, a data scientist is someone who can extract the meaning of the data and interpret it, which requires both tools and methods from statistics and automatic learning, while being human. She spends a lot of time in the process of collecting, cleaning and collecting data, because the data is never clean. This process requires persistence, statistics and software engineering skills – skills that are also necessary to understand the bias in the data and debug log output from the code.

what does data scientist do

Once it gets the data in shape, a crucial part is the exploratory data analysis, which combines visualization and data sense. She will find patterns, construction patterns and algorithms-some with the intention of understanding the product’s use and overall health of the product, and others to serve as prototypes that eventually get cooked back into the product. It can design experiments and is an essential part of decision-making based on data. She will communicate with team members, engineers and leadership in clear language and with visualizations of data so that even though her colleagues are not immersed in the data themselves

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Similar to a business / data analyst, data scientists combine knowledge of computing and applications, modeling, statistics, analysis and mathematics to discover knowledge about data. Evolving beyond the data / business analyst, the data specialist takes these ideas and combines them with a keen business sense and effective communication to change the way an organization approach challenges.

The average day of a data scientist involves retrieving data from multiple sources, running through a scanning platform, and then creating data visualizations. They will spend hours cleaning and analyzing data from different angles, looking for trends that highlight problems or opportunities. All views are communicated to business and IT leaders with recommendations for adapting existing business strategies.

For example, they might discover a section of consumers behaving differently. After further analysis, they discover this sub-section of consumers share a similar trait. They can then recommend ideas to change the behavior of the consumer.

Data Scientists do specific tasks include:

  • Identify data analysis issues that offer the best opportunities for the organization.
  • Determine the correct datasets and variables.
  • Collection of large structured and unstructured data sets from disparate sources.
  • Data cleansing and validation to ensure accuracy, completeness and consistency.
  • Collection of large structured and unstructured data sets from disparate sources.
  • Design and implement models and algorithms to operate large data stores.
  • Analyze data to identify trends and patterns.
  • Interpret data to discover solutions and opportunities.
  • Communicate results to stakeholders using visualization.

Would you make a good data scientist?

    To find out, ask yourself: Do you . .

  • Have a degree in Mathematics, Statistics, Computer Science, Management Information Systems or Marketing?
  • Have considerable work experience in any of these areas?
  • Interested in collecting and analyzing data?
  • To work individualized and solve problems?

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  • communicate well both verbally and visually?
  • Want to expand your skills and meet new challenges?

Skills required to become a data scientist?

This is the core set of 8 data science competencies you should develop:

Basic Tools: Whatever type of business you are interviewing, you are likely to be expected to know how to use the tools of the trade. This means a statistical programming language, such as R or Python, and a database querying the language as SQL.

Basic statistics: At least a basic understanding of statistics is vital as a data researcher. One interviewer told me once that many of the people he interviewed could not even provide the correct definition of a p-value. You must be familiar with statistical tests, distributions, maximum likelihood estimators, and so on. Think about your basic stats class! This will also be the case for automatic learning, but one of the most important aspects of your statistical knowledge will be understanding when different techniques are (or are not) a valid approach. Statistics are important for all types of businesses but especially for data-driven companies where the product is not data-driven and product stakeholders will depend on your help to make decisions and design / evaluate experiences.

Machine learning: If you are in a large company with huge amounts of data or if you are working in a company where the product itself is particularly data-driven, you may want to know the automatic learning methods . This can mean things like the nearest k-neighbors, random forests, overall methods – all the buzzwords learning machine. It is true that many of these techniques can be implemented using R or Python libraries – because of this, it is not necessarily a dealbreaker if you are not the leading global expert on how algorithms work. More important is to understand the amplitudes and really understand when it is appropriate to use different techniques.

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Multivariable Computation and Linear Algebra: You can actually be prompted to derive some of the automatic learning or statistical results that you use elsewhere in your interview. Even if you are not, your interviewer can ask you questions of basic computation or linear algebra, as they form the basis of many of these techniques. You may be wondering why a data scientist would need to understand this stuff if there are a lot of offline implementations in sklearn or R. The answer is that at some point it may be worth it For a data science team to build their own implementations internally. Understanding these concepts is the most important in companies where the product is defined by data and small improvements in predictive performance or algorithmic optimization can lead to huge wins for the company.

Data Munging: Often the data you are analyzing will be messy and hard to work with. For this reason, it is really important to know how to deal with data imperfections. Examples of data imperfections include missing values, inconsistent formatting of strings (eg ‘New York’ versus ‘new york’ versus ‘ny’) and formatting the date ( ‘2014-01-01’ Vs. ’01 / 01/2014 ‘, Unix time vs. timestamps, etc.). This will be the most important in small businesses where you are starting to rent data, or data from companies where the product is not related to the data (especially because the latter often grew quickly with little attention To the cleanliness of the data), but this skill is important for everyone to have.

Data visualization and communication: Data visualization and communication are extremely important, especially for start-ups that make data-driven decisions for the first time or for companies where data scientists are seen as helping Making decisions based on the data. When it comes to communicating, this means describing your results or how the techniques work for the public, both technical and non-technical. Visualization wise, it can be extremely useful to familiarize yourself with data visualization tools like ggplot and d3.js. It is important not only to familiarize yourself with the tools necessary to visualize the data, but also the principles behind the visual encoding of the data and the communication of information.

Software Engineering: If you are interviewing a small business and are one of the first recruits of data scientist, it may be important to have a solid background in software engineering. You will be responsible for managing a large number of data records, and potentially developing products based on data.

Think like a data scientist: Companies want to see that you are a problem solver (data). That is, at some point during your interview you will probably be asked about a high-level problem – for example, a test that the company might want to perform or a product driven by the data – He might want to develop. It is important to think about what is important and what is not important. How should you, as a data scientist, interact with engineers and product managers? What methods should you use? When are the approximations logical?

Source :wisconsin


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