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Projects on Data Science that will Get You Hired Easily in 2019

Are you a Data Science aspirant, constantly gaining knowledge by taking number of online courses and reading a bunch of books?

So, what’s next? Am sure, you would be looking forward in starting your challenging career in Data Science. But we all know that getting a job in Data science is intimidating. Therefore, the best way of showcasing your skills to your hiring manager with a portfolio of data science projects will help you to hire easily in 2019.

So to help you out, below are the projects on Data Science that proves your employer that you have potential to apply data science skills in real-time projects.

So, what are you waiting for? Know the top 5 types of data science projects that will boost your portfolio, and in turn help you to land in a challenging data science job.

1.Data Cleaning:

It is estimated that 80% of the Data scientists spends their time on a new project cleaning data. As this is a huge pain point for data science teams.

So, if  you have a proven experience in cleaning data, then you can increase your chances of getting hired by a top company and you’ll be considered more valuable.

Thus, before working on the data cleaning project, find some messy data sets, and then start cleaning.

So now you have your data, it’s time for you to pick an appropriate tool.

For instance: If you’re working with Python, then Pandas is a great library for you to  use.

In case, you’re working with R, then you can use the dplyr package. As this package is great because, it uses a “grammar of data manipulation.”

Therefore, you’ve got data and also the right tools. So now what?

Make sure to showcase the below skills in the project:

  1. Import data
  2. Join multiple data sets
  3. Detect missing values
  4. Look for anomalies (1, 2, 3, “Fred”?)
  5. Impute for missing values
  6. Data quality assurance

As this acts as a very general outline for you to get  started.

2.Exploratory Data Analysis:

Another important aspect of a data science is Exploratory Data Analysis (EDA).

As this process involves generating questions, and then investigating those questions with visualizations.

As Exploratory Data Analysis (EDA) allows an analyst to predict conclusions from the collected data to drive business impact.

It can also include some interesting insights based on the customer segments, or sales trends depending on seasonal effects.

So for an EDA project with Python, you can use the Matplotlib and Pandas library.

For R users, you can use ggplot2 package, which is considered to be useful.

Like dplyr,  ggplot2 package  also utilizes a “grammar of” strategy. But this time it’s a grammar of graphics.

So, to build an  EDA project, you should possess the following skills:

  • Must have the capability to formulate relevant questions for investigation
  • Having skill in identifying trends
  • competence in Identifying covariation between variables
  • Must be skilled in Communicating results effectively using visualizations such as scatterplots, histograms, box and whisker, etc.

3.Interactive Data Visualizations:

Interactive data visualizations(IDV)  include tools such as dashboards.

Dashboards are useful for both data science teams, as well as for more business-oriented end users. As Dashboards allow data science teams to collaborate, and depict insights together.

Moreover, Dashboards acts as an interactive tool for business-oriented customers. As these individuals mainly focus on strategic goals rather than technical details.

So more often, the deliverable for a data science project to a client will be in the form of a dashboard.

So, if you’re a  Python users, you can use the  Bokeh and Plotly libraries, which is great for creating dashboards.

In case, you’re an R users, remember to check out RStudio’s Shiny package.

Thus, in your dashboard project, you should possess these important skills:

  • Having skill at including metrics that are relevant to your customer’s needs
  • Having knowledge on creating useful features
  • Competence in building logical layout (“F-pattern” for easy scanning)
  • Ability to create an optimum refresh rate
  • Capability to generate reports and other automated actions.

4.Machine Learning:

A machine learning project is being another important piece of your data science portfolio.

So before you start building some deep learning project, take a minute to step back. That is, rather than creating a complex machine learning model, first start with the basics.

Hence, for a great beginning, start using Linear regression and logistic regression . As these models are easier to interpret and communicate to the upper level management.

I would also highly recommend you to focus on a particular project that has a great business impact, such as predicting customer churn, fraud detection, or loan default.

As these acts as a more real-world than estimating flower type.

So, if you’re a Python user, use the Scikit-learn library.  As this library covers a ton of useful machine learning topics. Below are the few:

  • Classification
  • Regression
  • Clustering
  • Dimensionality reduction
  • Model selection
  • Preprocessing

So, if you’re an R users, use the Caret package.

In your machine learning project, you must possess the following skills:

  • Reason why you have chosen to use a specific machine learning model
  • To avoid overfitting, you must be skilled in Splitting data into training/test sets (k-fold cross validation)
  • Must have knowledge in selecting the right evaluation metrics (AUC, adj-R^2, confusion matrix, etc.)
  • Feature engineering and selection
  • Hyperparameter tuning

5.Communication:

Communication is considered as an important aspect of data science. As effectively communicated results is the one which separates the good data scientists from the great ones.

Also, you must know one thing, it doesn’t matter how fancy your model is. If you’re not able to  describe it clearly it to your teammates or customers, then there is no chance for you to get their buy-in. Here, Slides and notebooks, both acts as a great communication tools.

Thus, use one of your machine learning projects and present it into slide format. For this, you can also use a Jupyter Notebook or RMarkdown file for an effective communication project.

Then using free Github Pages, you can then convert these markdown files to static websites . As this acts as a  great way for you to showcase your portfolio to your potential employers.

So, before presenting, make sure to understand who your intended audience is. And you know, Presenting a project to executives is completely different than presenting to machine learning experts. So below are some skills that you must possess:

  • Know who’s your intended audience
  • Try to present only the relevant visualizations
  • Try to avoid more slides with too much information
  • Make sure that your presentation flows in a proper manner.
  • Check the results for a business impact (reduced cost, increased revenue)

Final thoughts:

As we above discussed are the best Data Science portfolio projects that will boost your knowledge, skills and your Data Science career too!!

So, if you’re a beginner, then you can start doing project on  Movie Recommendation System , Face recognition, Customer Segmentation using Machine Learning algorithm, Sentiment Analysis Model in R, Uber Data Analysis and Credit Card Fraud Detection Project in R.

Hence, the ball is in your court, start working on these projects. Additionally if you need practical knowledge in Data Science, take advantage of Data science course from a reputed training institute in order to gain mastery in Data Science and get placed in your dream job!!

Happy job hunting!


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