Data Science Tools To Consider Using
The key ingredient for a successful data scientist is a curious, skeptical, and innovative mind. However, data scientists also need to be familiar with the tools of the trade, various technologies, which can be broken down into the following categories:
Moving Beyond the Tools
Keep in mind that tools are just that — devices for reducing the amount of work required. Choosing the best tools available is certainly important. For example, many organizations currently struggle to manage “big data” because they made huge investments in on-premises data warehouses that run Hadoop, which requires highly specialized database administrators (DBAs) just to keep it up and running. However, choosing the right tools is only a small part of extracting value from data. Organizations also need to come up with creative uses for that data.
Imagine you manage a website for connecting prospective car buyers to dealers. You build hundreds of information tags into the website that indicate when a shopper is hovering over a clicking a link. All the data flows into your data warehouse, representing several terabytes of data per week. You’ve collected historical data spanning several years. Collecting the data was relatively easy. The challenge is figuring out what to do with that data.
This seems like a common challenge for many organizations starting out in data science. They approach it mostly as an operational challenge and focus on collecting the data because it’s relatively cheap and easy to understand. It’s meeting-friendly and everyone can get behind the effort. They’ll even create multiple clusters or data lakes to pool their data from across the organization. But that’s the easy part. What organizations struggle with is the science of data science. They’re unaccustomed to asking and answering interesting questions.
As the data scientist for this imaginary car website, think about the questions you could ask to extract value from the data. You could examine the data to determine whether customers were more likely to click on a car if it were red, blue, or yellow. If the reports showed that customers are 2% more likely to click on a car if it’s red, the organization could share that with car dealerships to increase their sales and revenue. You could also experiment with the number of cars listed on each page to determine the optimum number of listings per page.
As a data scientist, this is the type of empirical research you should be considering. Using the tools at your disposal, you need to ask interesting questions, run experiments, and produce well-designed reports to convey the newfound knowledge and insight to your organization’s decision-makers and stakeholders.
Remember, it’s less about the tools and more about what you can do with those tools. A master carpenter can do wonders with a hammer, a handsaw, and a few other basic tools and materials, whereas someone unskilled in carpentry would have trouble building a simple chair in a fully-equipped woodworking shop. As a data scientist, you want it all — the skills and expertise along with the best tools available!
Frequently Asked Questions
Why is Python considered an essential data science tool?
Python is considered an essential data science tool due to its simplicity, readability, and extensive library support for data science and machine learning tasks. Libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn make it easier for data scientists to perform data analysis, data preprocessing, and machine learning.
What are the benefits of using open-source data science tools?
Open-source data science tools offer several benefits, including cost-efficiency, flexibility, and a supportive community. Popular open-source data science tools like Python, R, and Jupyter Notebooks enable data scientists to create, modify, and share code easily, facilitating collaborative and reproducible research.
How do data visualization tools help data scientists?
Data visualization tools help data scientists by allowing them to visualize data in a manner that is easy to understand and interpret. Tools like Tableau, Matplotlib, and Power BI enable effective data storytelling, making complex data more accessible and insightful for stakeholders.
领英推荐
What are some essential data science tools for big data analytics?
Essential data science tools for big data analytics include Apache Hadoop, Apache Spark, and Apache Hive. These tools allow data scientists to process and analyze large data sets efficiently, providing valuable insights from big data sources.
How can Jupyter Notebooks be used by data scientists?
Jupyter Notebooks can be used by data scientists for interactive data analysis, data visualization, and sharing of results. This tool allows data scientists to combine code, text, and visualizations in a single document, making it easier to conduct exploratory data analysis and communicate findings.
What makes R a popular tool among data scientists?
R is a popular tool among data scientists due to its powerful statistical analysis capabilities and extensive library of packages. It provides a comprehensive environment for data manipulation, data analysis, and data visualization, making it an ideal choice for data science applications.
How do data science tools and frameworks enable data scientists?
Data science tools and frameworks enable data scientists by providing pre-built functions, libraries, and structures that streamline the data analysis process. They facilitate data collection, data preparation, model building, and result interpretation, making it easier for data scientists to focus on deriving insights rather than handling technical complexities.
Why is data preprocessing important in data science tasks?
Data preprocessing is crucial in data science tasks because it involves cleaning, transforming, and organizing raw data into a usable format. This step ensures the accuracy and quality of the data, which is essential for reliable data analysis and model performance in data science and machine learning.
What are some popular data visualization tools used by data scientists?
Some popular data visualization tools used by data scientists include Tableau, Matplotlib, Power BI, and D3.js. These tools help in creating interactive and compelling visualizations, making it easier to interpret and communicate complex data insights.
This is my weekly newsletter that I call The Deep End because I want to go deeper than results you’ll see from searches or AI, incorporating insights from the history of data and data science. Each week I’ll go deep to explain a topic that’s relevant to people who work with technology. I’ll be posting about artificial intelligence, data science, and data ethics.?
This newsletter is 100% human written ?? (* aside from a quick run through grammar and spell check).
More sources:
--
1 个月Very informative, thanks Doug I am a tech enthusiast learning AI/ML would need some tutoring to learn some AI/ML tools.