Data Science Consulting Services

A data scientist works closely with business and data engineers. Its role is to comprehensively analyze the issue: from understanding it through preparing and processing data to building a model, visualizing it and creating a recommendation based on the analysis result.

A data scientist must therefore be perfectly at home in the field of statistics. Most machine learning algorithms are based on calculus, linear and nonlinear algebra.

A data scientist is expected to be able to prepare a good data visualization and to be a communicative person. This feature is important because the data scientist must collaborate with people, present their models and discoveries in the company, and encourage colleagues to use them.

10 must-have skills of any professional data scientist:

1. Statistics
2. Programming
3. Advance knowledge of Data Science
4. Data Visualization
5. Data Analysis
6. Big Data
7. Fundamentals of machine learning
8. Deployment of models
9. Good communication Skills
10. Problem-solving thinking

Software engineer builds software, maintains it, tests it, and improves it when needed. Software engineering, therefore, creates a base that every data scientist uses in their work. That’s why it’s entirely justified to say that there is no data science without software engineering.

Data scientists analyze, process, and model data they possess. Then, they interpret the results to create actionable plans and insights for companies and other organizations.

Data science is a multi-role field of expertise that entails an understanding of such disciplines as:

• Computer science
• Maths
• Statistics
• Business knowledge
• Software development
• Machine learning

The list of desirable skills one should possess is even longer, and it comprises i.a.:

• Programming
• Data visualization and reporting
• Statistical analysis
• Effective communication and soft skills
• Data mining, storing, analyzing
• Knowledge of the big data platforms
• Familiar with the cloud computing tools
• Knowledge of the data warehousing

In its essence, data science is all about five fundamental applications:

1. Identifying trends and patterns in large datasets. Data science is the best and most effective way to analyze large datasets in order to draw useful conclusions from them.
2. Identifying anomalies. Anomalies are frequent occurrences in various industries and sectors. Especially banking and e-commerce companies should be interested in identifying anomalies. For instance, if a bank detects some unusual activity on their client’s account, they can take necessary steps to prevent, for example, money laundering.
3. Predicting the likelihood of future events. Predictive analytics can take into account many elements and datasets to present you with an accurate prediction about future events or courses of events.
4. Showing how things are connected to one another.
5. Categorizing individual data points. Classification is all about identifying to which of a set of categories a new observation belongs, on the basis of a training dataset containing observations (or instances) whose category membership is known. Classification is essential, for instance, in spam detection.

To sum up, data science helps you work more efficiently and, in many instances, quicker as well. Thanks to data science, your company can understand data and draw useful business-wise conclusions, which, in turn, leads to more accurate and informed decisions.

To learn more, read our article: Data science for business – what problems can be easily solved?

Yes!

In fact, data science is one of the fastest-growing fields in recent years. According to the Bureau of Labor Statistics, data science is projected to grow 31% over the next ten years.

The possible uses of data science in business include:

Decision making

As we have previously mentioned, the main purpose of data science is finding patterns and drawing conclusions from the organization’s data. Thanks to data science, answers to important business questions and solutions to problems occurring in the company can be easily found.

Security

The popularity of ML models can be justified by the fact that data scientists adjust them according to the current needs of the company. That is why the majority of organizations create models which process huge amounts of data in real-time and are able to immediately find recurring patterns and detect possible anomalies or errors which would normally go unnoticed by a human eye.

It is especially useful for security purposes. In case of behaviors on users’ accounts that seem to alert, the system can notify the administrator who is able to take immediate action and check whether a danger was real.

Sales

Another area that can benefit from data science is sales. Since by analyzing data, you can predict future events, trends, and behavior, it is also possible to forecast sales in upcoming months. Thanks to data science, sellers may avoid overproduction or run out of the most popular supplies.

Naturally, the way in which a company will benefit from data science is dependent on the industry and individual needs of the company. For example, financial institutions are usually most interested in assessing risks of investments and ensuring security, while e-commerce businesses will be focused on controlling stock and marketing purposes.

Read more in our article: Data science vs. Computer science

Companies need data science consulting to get the most out of their data.

It helps them:

• Make a deep market analysis,
• Compare their position with the competition,
• Make the right business decisions based on accurate information,
• Improve the quality of customer service,
• Save money on operating costs,
• Streamline various processes in the company.