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May 15, 2023
16 min
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Svetlana Lavrinenko

Data Science Tools Make Businesses Stronger

May 15, 2023
16 min
Svetlana Lavrinenko photo
Svetlana Lavrinenko
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Data Science has gone from a trendy combination of words to something more. And beneficial for those who need it. After all, the business analytics data is ordinary — prices, registration emails, and the number of likes. And conclusions from standard data can be very unusual. Data scientists do this with unique methods, technologies, or custom tools — converted to a specific order case. With the help of data science tools, you can collect, process, add up and analyze data about competitors and attitudes toward your brand or get a valid business forecast from a given perspective. It is so, as in a real workshop: first, decide what to do. Then — how? and in the end — how to do it better?

What Does Data Science Do?

The big data science process is a systematic approach to solving the problem of gaining insights from data analysis. It provides a structured framework for formulating a question, deciding how to solve it, and then presenting the solution to stakeholders. Another name for this process is the data science lifecycle. The terms can be widely used interchangeably, and both describe a workflow that starts with data collection and ends with model deployment.

In turn, computer science encompasses both theoretical and practical aspects of computer technology, including designing, developing, and analyzing algorithms, programming languages, software, and hardware.

Data Science Strategic Tools

Choosing a data analytics tool (desktop or cloud-based) is an important decision that will change the strategic implications for businesses for years. A data science tool that doesn't match the tasks and needs can confuse and prevent team members from revealing valuable data. And properly selected and "sharpened" tools help to get an idea about customers and products and direct decision-making in the right direction.

Data Science Tools Popularity

Data Science Tools Popularity, animated - KDnuggets

Data Science Process Is a Plan of Action

Data science is a spiral on which each turn is a specific order of actions at a given level. After agreeing on the results, the process moves to the high level, and so on, until the project is completed successfully.

The sequence of actions is as follows:

  1. Discovery: Gathering complex data from all established internal and external sources, including data manipulation techniques. It can be:
  • Logs from web servers
  • Data from social networks
  • Census results
  • Data from online sources using API.
  1. Preparation: Clear data from inconsistencies: missing values, empty columns, incorrect format. Before modeling, it is necessary to process, study and prepare the data. The cleaner the results, the more accurate the predictions for data science.
  2. Model development: Definition of a method and technique for building relationships between input variables. Model planning is performed using statistical formulas and visualization tools. SQL Analysis Services, R, and SAS/access are used.
  3. Model building: The data scientist distributes sets of information for learning and testing. Techniques such as association, classification, and clustering are applied to the learning data. The prepared model is checked on a "test" dataset.
  4. Deployment: Presentation of the final base model with reports, code, and technical documentation. The model is deployed to the production environment in real-time within data science.
  5. Communication: informing and exchanging opinions of stakeholders: whether the results of this stage of the project are successful based on the model's input data.

High-quality communication with the customer is one of the familiar places of all companies involved in data science, particularly DATAFOREST.

Data Science Process Is a Plan of Action

4 Important Questions

To choose the right tool for working with data science, you need to answer four questions:

  1. How does your company feel about data?

Knowing what you need from a data analysis tool will help you formulate a list of requirements from a service provider. Knowing where the data is stored by cloud computing will answer the question, "Can this tool work with the data in its current state?" You may even find that the data is unsuitable for answering business questions — even with adding a data analysis tool. Poor quality or inaccessible data can limit opportunities.

  1. Who will use the data analysis tool?

Top executives, product managers, developers, marketers, and others rely on data analytics to make department decisions. So, choose a data analysis tool that can suit all needs and can integrate with their data sources.

  1. What skills are required to use the tool?

Some companies may have data scientists who can handle complex SQL queries and tools. But this is optional; you can additionally teach existing employees. And the best option would be a tool that is easy to use and open to access to data.

  1. How important is data visualization?

Each analysis tool is capable of interpreting the data. The presentation of pins varies from platform to platform. A tool must deliver results in an easy-to-understand manner to ensure the business will reap the full benefits of the tool. The team will feel insecure when making decisions based on confusing graphs and charts.

The more reliable data analysis tools are, the better you can use insights for your business needs. Answers to difficult data science questions appear after a qualitative analysis of pure data.

Different Data Science Tools for a Common Purpose

Each step in advancing data science to insight for businesses requires separate tools. They differ in the way they work in the data science process. To collect info, web scraping or API exposure is needed; to store it, you need warehousing; and then — analysis, Machine Learning, and visualization. These are the shelves on which data science tools lie. And what exactly is required — to hammer in a nail or lay a tile — is up to the customer to decide.

No hit parades

You can search for "data science tools" and get many hit parade pages of the best, most popular, and newest tools. But the leading positions by names will not overlap. Because each author writes about their practice, and it appears in all its diversity when choosing the right tool. The tools below are sorted by their data science stage.

First of all, you need to decide on a programming language. According to the task, Python and R Language are two equivalent options.

  • Experienced programmers will take time to get used to R, while Python is more familiar, with a few exceptions.
  • Python is closer to production and more often used in commercial projects. In academic circles, R is more popular.
  • Upgrading your horizons in Machine Learning methods — you need R. If you just get acquainted with the most popular methods, Python has more opportunities.
  • Python is better suited if the task is to implement development and programming something more complicated than predictors.

The choice of language also affects data analysis tools.

Web scraping versus API data management is no longer such a thing — both data science tools can be used separately or together. The only difference is that the API technology is controlled by the website from which you want to get data. And web scraping does not require technical support from the site.

Data visualization summarizes valuable information that the team might not find otherwise. Apache products are quantitatively leading against Amazon and Microsoft Azure in data warehousing.

According to Forbes magazine, the best data science visualization tools as of 2023 are nominated as follows:

  • Microsoft Power BI: Best for business intelligence (BI)
  • Tableau: Best for interactive charts
  • Qlik Sense: Best for artificial intelligence (AI)
  • Klipfolio: Best for custom dashboards
  • Looker: Best for visualization options
  • Zoho Analytics: Best for Zoho users
  • Domo: Best for custom data science applications

Business intelligence (BI) is software that takes business data and presents it in user-friendly views such as charts, reports, dashboards, and graphs.

Difference Between Data Science with BI

Different Data Science Tools for a Common Purpose

Data Science at a Crossroads: Which Technique to Choose?

Methods, like data science tools, are best-chosen for each case. But there is a list that most data scientists use, and these techniques appear in most solutions.

  1. Classification is the sorting of data into groups or categories. The software is trained to identify and sort data. Known data sets are used to build data science decision-making algorithms by a computer that quickly processes and classifies the data.
  2. Regression is a method of finding relationships between unrelated data points. Communication is usually modeled based on a mathematical formula and presented as a graph or curve. When the value of one data point is known, regression is used to predict another point.
  3. Clustering is a data science method of grouping related data to look for patterns and anomalies. Clustering differs from sorting because data cannot be neatly categorized into categories. It means that the data is grouped into the most probable ratios. Using clustering, you can discover new patterns and relationships.

Data scientists use computing systems to track data processing progress.

What to Look for When Choosing a Data Science Technique?

There are several factors that you should consider, including:

  • Different data science techniques are better suited to specific types of problems. Classification techniques are used for situations where the goal is to assign data points to predefined categories, while regression techniques are used for problems where the goal is to predict a continuous value.
  • The type and size of data you are working with will influence the suitable techniques. For example, deep learning techniques may be more appropriate for image or speech data, while statistical methods may be better for small, structured datasets.
  • The availability of tools and resources can impact your choice of data science techniques. For example, you may need to choose computationally efficient methods if you have limited access to computing resources.
  • It is essential to consider the business goals and constraints of the project when choosing techniques. If the goal is to develop a predictive model that can be used in real-time, you may need to choose optimized speed techniques.
  • Your expertise and experience in data science will also influence your choice. If you have more experience with ML techniques than statistical analysis, you may be more likely to choose the first for a particular project.

It is important to carefully evaluate your options and choose data science techniques appropriate for your problem, data, and resources.

Two? Combinations of data science techniques

Different data science techniques can be combined to solve complex problems and gain deeper insights from data.

  • Preprocessing and feature engineering with Machine Learning: Before building a Machine Learning model, data must be cleaned, transformed, and preprocessed. Additionally, feature engineering techniques can be used in data science to create new critical features from the existing data, which can improve the Machine Learning model's performance.
  • Exploratory data analysis with data visualization: Exploratory data analysis involves examining and understanding the structure and patterns of the data. Data visualization techniques such as scatter plots, histograms, and heat maps can be used to create visualizations that reveal patterns and relationships within the data.
  • Statistical analysis and hypothesis testing with Machine Learning: Statistical techniques such as hypothesis testing can be used to make inferences about the data and test hypotheses. Machine Learning techniques can be used to build predictive data science models based on the data and test the accuracy of the predictions.
  • Deep learning with natural language processing: Deep learning techniques such as recurrent neural networks and convolutional neural networks can be combined with natural language processing techniques to build models that can analyze and generate human language.
  • Ensemble learning with Machine Learning: Ensemble learning techniques can combine the predictions of multiple Machine Learning models, improving the overall accuracy and robustness of the data science model.

The specific techniques and combinations will depend on the problem and the available data.

Examples of Good Data Science Technique Choices

Below are top-5 of companies that have successfully implemented data science techniques to improve their business operations:

  1. Amazon has successfully implemented data science techniques to improve its recommendations system. By analyzing data, Amazon can recommend products customers are more likely to buy, improving satisfaction and driving sales.
  2. Netflix has used data science techniques to improve its content system. By analyzing customer viewing history and preferences, Netflix is able to recommend shows and movies that customers enjoy, improving the UX and increasing engagement.
  3. By analyzing real-time data on ride demand and driver availability, Uber can adjust prices dynamically and match riders with drivers, improving efficiency and reducing wait time series.
  4. Capital One: by analyzing transaction data and identifying patterns and anomalies, the company is able to detect and prevent fraudulent transactions, improving the security and reliability of its services.
  5. By data science analyzing user queries and searches, Google can deliver more relevant and accurate search results, increasing the UX and improving user satisfaction.

These are just a few examples, and we suggest discussing your case with DATAFOREST.

Breaking down data science interview questions by category

Analysis of Data Science Interview Questions | by Vimarsh Karbhari | Acing  AI | Medium

Right On the Bull's-Eye

Best practices in data science refer to the set of guidelines and procedures that effectively achieve successful outcomes in data science projects. These practices are developed based on the experience and expertise of data science professionals and are constantly updated as new techniques and technologies emerge. The goal of best practices in data science is to ensure that projects are well-organized and efficient in delivering meaningful insights and value to businesses.

Some key best practices

  • It is essential to define clear and specific goals for the data science project, including the problem to be solved, the data to be used, and the expected outcomes.
  • A structured approach, such as the Cross-Industry Standard Process for Data Mining methodology, can convince stakeholders to give launch consent.
  • Ensuring data quality and preparing the data for analysis is critical in any data science project. It includes cleaning, transforming, validating, and handling missing or inconsistent data.
  • Exploratory data analysis techniques can help to identify patterns and relationships in the data and guide subsequent analysis.
  • Choose the most appropriate data science techniques based on the nature of the problem, data type, and resources available. It may involve a combination of statistical, Machine Learning, and deep learning techniques.
  • Model validation is a vital step to ensure that the models are accurate.
  • Communication of the project results is vital to ensure stakeholders can understand and act on the insights gained from the analysis.
  • Continuous improvement and iteration are important to refine and improve the models and insights over time.

Following these practices helps data scientists to ensure that their projects help deliver insights and value to the brand.

What exactly leads to success in data science?

Here are a few examples of how the best data science practices can help companies succeed:

  1. Data science helps companies to make better decisions by providing insights and recommendations based on data. A retail company may use customer data to identify patterns and trends in buying behavior and use this information to make more informed decisions about inventory and digital marketing strategies.
  2. By using data science techniques to analyze customer behavior and preferences, companies can tailor their products and services to meet the needs of their clients. A healthcare provider may use predictive analytics to identify patients at risk of developing chronic conditions and provide personalized care plans to improve outcomes and patient satisfaction.
  3. Companies that use data science effectively can gain a competitive advantage by making better decisions, improving operations, and providing a superior customer experience. A financial services company may use Machine Learning algorithms to identify fraudulent transactions more quickly than competitors, reducing losses and improving customer trust.
  4. Data science can also enable brands to develop new products and services and identify new revenue streams. A media company may use natural language processing techniques to analyze social media data and identify trends, which can be used to develop new content and advertising strategies.
  5. Organizations can optimize their operations and reduce costs by using data science techniques such as predictive modeling and optimization. A manufacturing company may use predictive maintenance models to identify potential equipment failures before they occur, reducing downtime and maintenance costs.

Using data science effectively gains insights, optimizes operations, and develops innovative solutions to drive success.

Eight tips for successful practice implementation

  1. Create a data-driven culture
  2. Focus on business outcomes
  3. Use a structured data science approach
  4. Ensure data quality and preparation of data sets
  5. Select appropriate data science techniques
  6. Validate and iterate the model
  7. Communicate results effectively
  8. Invest in data science training and development

It is more convenient to work with the right tool

The usability of data science is directly proportional to speed and efficiency. So, more than just the thought of turning to data science is essential. It is equally vital to understand the essence of your business and choose the right tool for the right solution. Even to get a simple tool with the click of a button, only the customer will have to form tasks for it. It is a great responsibility. DATAFOREST invites you to share it and discuss your attitude to data science and your particular project. We are always waiting and in touch!

It is more convenient to work with the right tool

FAQ

What are the considerations for selecting data science tools and techniques?

Selecting the appropriate data science tools and techniques requires careful consideration of several factors, including the problem domain, data size, skill level, ability to perform data, compatibility, cost, and scalability.

How do I determine which programming language suits me best for a data science project?

Choosing a programming language for data science requires careful consideration of several factors, including the nature of your data, available libraries and tools, skill level, industry preferences, and job market demand.

What is the difference between open-source and proprietary data science tools, and which should I choose?

Open-source data tools can be a good option if you have a limited budget and are comfortable relying on community support. They are also an excellent tool customization option to fit your needs. Proprietary tools may be better if you require more robust customer support and more advanced features or if security and privacy are a concern.

What visualization tools and techniques would you recommend?

The best visualization tool and technique will depend on your specific needs and the data type you are working with. Choosing a tool and technique that effectively communicates your insights and engages your audience is important. Some famous (including open source software) visualization tools and techniques are Tableau, Python libraries, R ggplot2, D3.js, Geographic Information Systems software, and Infographics.

What are the popular statistical techniques and models used in data science?

Some of the most popular statistical techniques and models used in data science are Linear and Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, Naive Bayes, K-Nearest Neighbors, Principal Component Analysis, Clustering, and Neural Networks.

How to understand which Machine Learning algorithm is more suitable?

It would be best if you considered the following: define the problem that you are trying to solve; collect the relevant data and preprocess it; choose a subset of algorithms; split data into training and testing sets; train and evaluate models; choose the best algorithm.

What are the most effective methods of data cleaning and processing?

Effective data cleaning and processing methods with positive use cases are: Handling Missing Data, Removing Duplicates, Data Transformation, Outlier Detection, Feature Engineering, Text Preprocessing, and Data Integration.

What must you know to choose the right data warehouse and management for a data science project?

There are several factors to consider in storage details when making this decision: data volume and variety, data structure, analytical requirements, integration with existing systems, cost, scalability and flexibility, and security and compliance. When processed with the right tools, data becomes more valuable.

How can I keep up with changes in various data science tools and techniques?

To keep up with changes in data science tools and techniques, you must: participate in online communities, attend conferences and meetups, read industry publications, take online data science courses and tutorials, and join online training programs.

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