What are the steps to approach a data science project for a beginner?

7 min readMar 10, 2022

The six steps of the data science process are as follows:

Step 1: Define Problem Statement:

Before you even begin a Data Science project, you must define the problem you’re trying to solve. At this stage, you should be clear with the objectives of your project. The end product of a data science project should always target to solve business problems. so, it’s essential to understand the business needs.

Step 2: Data Collection:

As the name suggests at this stage you must acquire all the data needed to solve the problem. Collecting data is not very easy because most of the time you won’t find data sitting in a database, waiting for you. Instead, you’ll have to go out, do some research and collect the data or scrape it from the internet. many times, this step is time-consuming because the data is scattered among different sources such as:

• spreadsheets

• existing internal databases

• publicly available data

Step 3: Data Cleaning:

Data Cleaning may not seem interesting to many of you but let me tell you it is of lots of importance as it makes the further analysis of the data way smoother. In this step, you’ll need to transform the data into a clean format so that the machine learning algorithm can learn useful information from it. Data cleaning is the process of removing redundant, missing, duplicate and unnecessary data. This stage is considered to be one of the most time-consuming stages in Data Science. However, in order to prevent wrongful predictions, it is important to get rid of any inconsistencies in the data.

Step 4: Data Analysis and Exploration:

Once you’re done cleaning the data, it is time to get the inner Sherlock Holmes out. At this stage in a Data Science life-cycle, you must detect patterns and trends in the data. This is where you retrieve useful insights and study the behaviour of the data. At the end of this stage, you must start to form hypotheses about your data and the problem you are tackling. Exploratory data analysis (EDA) is also needed to know the characteristics of the data. Within this step, try to find answers to the following questions:

• What models have worked well for this type of problem?

• What features might be useful?

• How would we get this model into production?

• How would we evaluate the model? What metric(s) would we use?

Step 5: Data Modelling:

This stage is all about building a model that best solves your problem. A model can be a Machine Learning Algorithm that is trained and tested using the data. This stage always begins with a process called Data Splicing, where you split your entire data set into two proportions. One for training the model (training data set) and the other for testing the efficiency of the model (testing data set). This is followed by building the model by using the training data set and finally evaluating the model by using the test data set.

Step 6: Optimization and Deployment:

This is the last stage of the Data Science life-cycle. At this stage, data scientists try to improve the efficiency of the data model, so that it can make more accurate predictions. The end goal is to deploy the model into production or a production-like environment for final user acceptance. The users must validate the performance of the models and if there are any issues with the model then that must be fixed in this stage.

What are the sources we have for getting datasets for our project?

Top 8 Free Dataset Sources to Use for Data Science Projects

• Google Cloud Public Datasets.

• Amazon Web Services Open Data Registry.

• Data.gov.

• Kaggle.

• UCI Machine Learning Repository.

• National Centre for Environmental Information.

• Global Health Observatory.

• Earth data

What are the Top 6 Data Science Project Ideas for Beginners?

Sentiment Analysis Project:

Sentiment analysis is used to add emotional intelligence to systems. It is one of the Data Science Project Ideas that people start with when they wish to learn how to process text.

For example, when a user types in a comment on a video or a blog post, sentiment analysis can be used to determine if the comment is appreciative, disparaging, critical, etc. These can also be used to classify emails, messages, reviews, queries, etc.

Sentiment analysis can also be used to analyse and make sense of reviews, complaints, queries, emails, product descriptions, etc. For instance, we can use sentiment analysis to generate tags for such content as being negative, positive, neutral, etc.

Use Cases:

• For classifying emails as positive or negative

• For labelling tweets as positive or negative

• For categorizing emotions in speech-based audio

Fraud Detection Project:

Fraud detection is one of the most important Data Science Project Ideas and also one of the most challenging Data Science Projects for final year students. With many forms of online and digital transactions coming into wide use, the chances of them being fraudulent are getting quite high.

Since any form of digital transaction generates data regarding current and previous transactions, as well as customer purchase records, we can use these data and Data Science techniques to identify if these transactions are potentially fraudulent.

Use Cases:

• Credit card fraud detection

• Transaction records fraud detection

Image Classification Project:

Image classification is one of the Data Science Project Ideas that can be used to classify and tag images based on their content. Image classification is widely used in the fields of Science, Security, etc. This is also among the most important applications Data Science has, with traditional application programming, it is very difficult to classify images.

Earlier, it required a lot of time and research to generate complicated rules and image transformations to classify images, and it was still quite error-prone. With Data Science, we can create models by training them with many labelled images. Then, these models can generate Machine Learning classification rules on their own, and we can feed them new images to be classified.

Use Cases:

• Digit recognition system

• Face detection system

• Gender and age detection system

Chatbot Project in Python:

Chatbots are one of the most essential parts of any customer-centric app of the day. They help in the better tracking of customer issues, faster issue resolution, and generating commands using normal text.

For example, many bots on platforms such as Slack and GitHub allow us to perform certain tasks just by writing and sending them the requirements in the chatbot. Chatbots also help customers get a resolution to their grievances without any human interaction.

For example, food delivery apps like Zomato and Swiggy use chatbots to assist users in resolving common issues, including refunds, missing food items, incorrect items, etc.

Use Cases:

• Customer care using a chatbot

• Customer feedback using a chatbot

• Price quote generation using a chatbot

Brain Tumour Detection with Data Science:

There are many Data Science applications in the healthcare field as well. One of these is brain tumour detection. In this application, we take many labelled images of MRI scans and train a model using them. Once the model is trained well, we use it to check if an MRI image shows any chance of having a brain tumour.

To implement these kinds of Data Science Project Ideas, we need access to MRI scan images of the human brain. Thankfully, there are datasets available on Kaggle. All we have to do is use these images to train our model so that, when fed with similar images, it can classify them as having a brain tumour or not.

Though such models do not completely remove the need for a consultation from a domain expert, they do help doctors get a quick second opinion.

Use Cases:

• Brain tumour detection using MRI images

• Brain tumour detection using vital information

• Brain tumour detection using patient history

Traffic Sign Recognition:

Nowadays, one of the most popular applications of Data Science is self-driving cars. Although a self-driving car could be very difficult and expensive to work with, we can implement a specific and important feature required in a self-driving car, which is traffic sign recognition.

In this, we use the images of different traffic signs and label them, depicting what the signs are indicating. The more images there are, the more accurate the model will be, though it will take longer to train the model. We start by using Convolutional Neural Networks (CNNs) to build the model with images that are labelled with what a specific traffic signal is indicating. Next, our model will learn with the help of these images and labels. Then, when a new image is given as the input, the model will be able to classify it.

Use Cases:

• Gesture recognition system

• Sign language translator

• Product quality checking system

What are the Essential Data Science Skills for Your Resume and Career?

  • Probability & Statistics.
  • Multivariate Calculus & Linear Algebra.
  • Programming, Packages and Software — R, Python.
  • Data Analysis.
  • Machine Learning / Deep Learning Algorithms.
  • Big Data.
  • Predictive Models.
  • Data Visualization, Data Wrangling.
  • SAS.
  • Database Management.

Do let us know in the comments if you liked the content also do check out our blog series on Finance and Product Management. Do check this blog if you are searching for an Ultimate guide for your Job/ Internship. Stay tuned for more blogs on Data science. Keep Learning Keep shining.