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5 Tools to Speed Up Your Data Science Project Progress

Save yourself and your team some precious time.

When you first get into the realm of Data Science, you will probably be all by yourself. You will need to learn programming, maths, statistics, visualization, and data visualization all by yourself. In the beginning, the projects you will work on will be simple and small.

You will be the one collecting the data, cleaning it, analyzing it, developing the machine learning model, training it, and measure its performance; in short, you will be the one taking care of all aspects of the project, from start to end.

But then, you join a company and become part of a team, and most probably, you will be in charge of just one step of the project’s development. Then you will need to learn how to build upon others’ work, how to communicate with them, and how to work together all to build a successful project.

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And we all know that when we are given a new project, looking for tools to make our lives easy is probably not the first thing that will come to mind. After all, looking for data science tools is like a none-ending spiral; once you get in, it may take hours – sometimes days – to get out!

So, allow me to take that burden off your shoulder and put in front of you five tools that will help you increase your work efficiency and make your project progress faster, smoother, and much more enjoyable.

№1: Apache Kafka

Let’s kick off the list with a tool that is known in the community, Apache Kafka. Apache Kafka is an open-source event streaming for distributed teams. Apache Kafka offers a high-performance data pipeline, data integration, and streaming analytics. This tool was designed and built for real-time data allowing data scientists to store massive streams of records with accuracy and speed.

Using Apache Kafka allow you and your team – and company – to run multiple clusters on one or more servers and use these clusters to stream and categorize incoming data into topics, each with its own timestamp. It also offers several APIs for all your team needs, including consumer API, stream API, and Producer API.

№2: DataRobot

Whether you’re new to data science or an experienced one, this next tool is for you. DataRobot is a Machine Learning platform for data scientists of all skill levels. The platform allows you to build, train and deploy accurate models in no time. DataRobot uses large parallel processors allowing you to develop your models at ease using different resources from Python, R, Spark ML, and other open-source libraries.

DataRobot offers a various product to make your life better, such as DataRobot Cloud, which allows you to build state-of-the-art predictive models and extend them using AWS, and DataRobot Enterprise, which is a platform build for companies allowing them a flexible deployment of their models and a potent, secure on-demand customer platforms.

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№3: Trifacta

Next on our list is Trifacta, which is not just one tool; rather, it’s a collection of tools that saves companies and data scientists a lot of time, money, and resources while building data Science projects. Trifacta focuses on the main time-consuming step of a data project, which is data wrangling. It allows anyone to work more efficiently with data.

Trifacta offers an amazing data wrangler tool that helps you with your machine learning algorithms by giving you suggestions and transformation to efficiently, quickly, and accurately prepare your data for visualization and accurate analysis. Trifacta is able to do that because it’s powered by a high-performance engine designed especially for data wrangling. Trifacta also organizes events for data scientists, such as the upcoming Wrangler Summit taking place on April 7–9.

№4: Apache Spark

Apache Spark is a potent analytics and processing engine for large-scale, real-life data. Apache Spark offers various high-level APIs for different programming languages, including Python, R, and Java. It also offers support for high-level data analysis tools, such as Spark SQL for SQL, Spark MLlib for developing and deploying machine learning models, GraphX for graph visualization and processing, and finally, Structured Streaming for stream processing.

Using Apache Spark, you can access different data management sources such as Cassandra and S3. Finally, Apache Spark also offers 80+ operators allowing you to build a variety of parallel applications.

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№5: Cascading

Last but not least on today’s list is Cascading. Cascading is a platform for data scientists to build and develop Big Data applications on Apache Hadoop. Cascading is not only for developing solutions for large and complex problems; you can also use it to solve simple problems because it harnesses the power of system integration framework, data processing, and scheduling engines.

Applications developed on Apache Spark can be run and extended on MapReduce, Apache Flink, and Apache Tea. It also provides great support for Hadoop distributed teams.

Final Thoughts

Working on a team is not always easy; you’ve got to know how to cooperate and synchronize with each other; this gets more challenging if your team is distributed across that world, which existed even before COVID. When the team is divided across the world, with different time zones and native languages, any tool that could help bring the term together, speed up the work, and make it more efficient is always welcomed.

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I have never met a data scientist or anyone in an IT field who is like, "No, I don’t need any tool to speed and ease up my work." We all appreciate a little help, a tool that helps us speed the required tasks, the repetitive ones, allowing us to spend most of our time and ability on the tasks that require creativity and intelligence.

In this article, I recommended 5 tools that offer great help to teams working on data science projects. These tools will help you with data cleaning, data analysis, and even building, training, and testing machine learning models.


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