6 Important steps needed to be a data engineer

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With the exponential growth of AI, businesses are eager to hire skilled Data Scientists to help them grow. Aside from obtaining a Data Science Certification, it is always beneficial to have a few Data Science Projects on your resume. Theoretical knowledge is never sufficient. So, in this blog, you’ll learn how to use Data Science methodologies to solve real-world problems in practice. You can learn more about data science by taking up Data Science Projects at ProjectPro.

Life Cycle of a Data Science Project

Data Science, when combined with the right data, can be used to solve problems ranging from fraud detection and smart farming to predicting climate change and heart disease. That being said, data alone isn’t enough to solve a problem; you also need a strategy or method that will yield the most accurate results. This raises the question:

How Do You Address Data Science Issues?

The following steps can be used to solve a problem statement in Data Science:

  • Define the Problem Statement/Biz Requirement
  • Data Gathering and Cleaning
  • Exploration and analysis of data
  • Deployment and Optimization of Data Models

Let’s take a closer look at each of these steps:

Step 1: Create a Problem Statement

Before you start a Data Science project, you must first define the problem you’re attempting to solve. At this point, you should be clear on your project’s goals.

Step 2: Gathering Data

As the name implies, at this stage you must collect all of the data required to solve the problem. Data collection is difficult because most of the time you will not find data waiting for you in a database. Instead, you’ll have to go out and collect the data yourself, or scrape it from the internet.

Step 3: Clean Up the Data

If you ask a Data Scientist what their least favorite process in Data Science is, they will most likely say Data Cleaning. The process of removing redundant, missing, duplicate, and unnecessary data is known as data cleaning. This stage is regarded as one of the most time-consuming in Data Science. However, in order to avoid incorrect predictions, any inconsistencies in the data must be removed.

Step 4: Data Exploration and Analysis

When you’ve finished cleaning up the data, it’s time to channel your inner Sherlock Holmes. You must detect patterns and trends in the data at this stage of the Data Science life-cycle. This is where you get useful insights and study the data’s behavior. By the end of this stage, you should have begun to form hypotheses about your data and the problem you are attempting to solve.

Step 5: Data Modeling

This stage focuses on developing a model that best solves your problem. A model is a Machine Learning Algorithm that has been trained and tested with data. This stage is always preceded by a procedure known as Data Splicing, in which you divide your entire data set into two proportions. One for training the model (training data set) and one for testing the model’s efficiency (testing data set). The model is then built using the training data set, and it is finally evaluated using the test data set.

Step 6: Deployment and Optimization:

The Data Science life-cycle has reached its conclusion. At this point, you should try to improve the data model’s efficiency so that it can make more accurate predictions. The model will eventually be deployed into a production or production-like environment for final user acceptance. Users must validate the performance of the models, and any issues with the model must be resolved at this stage. Now that you understand how Data Science can be used to solve problems, let’s get to the fun part. In the section that follows, I will provide you with five high-level Data Science projects that will help you get hired at top IT firms.

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