Data Science Project Good for your career

MLK Community Blog
  • Text To Speech Analytics (DATA SCRAPPING) :

    You will be surprised to know that the first task you may get as a data scientist will that be of data extraction & cleansing. As per reports, a data scientist generally spends 80% of his/her time on cleaning data, especially during new projects. A team is always on the lookout for people who can take this responsibility off their shoulder. Your proven capability at this task may get you instant entry in different projects and teams.As you already know, the most popular languages in the world of data science are Python and R. So, if you decide to make your data cleaning project in Python, we suggest you use Pandas Similarly, if it is R which is your choice, then try the dplyr package.Also, ensure that your project gives priority to the following skills
    –  Data Import
    – Working with multiple datasets
    – Finding out missing values
    – Finding out anomalies
    – Maintaining Data Quality and assurance.


  • Statistics Analysis (EXPLORATORY DATA ANALYSIS(EDA)):

    Data science is all about extracting insights and visualizing solutions to business problems. EDA or exploratory data analysis helps a data scientist to derive meaningful conclusions from the pool of data which help in complex decision-making. With EDA, a data scientist can learn behavioral patterns of certain customer segments or the way sales trends behave.Our suggestion is to go through Pandasand Matplotlib for creating a Python project is exploratory data analysis. If it is R which you prefer, you can ggplot2.Ideally,  Your EDA project should be able to prove the following objective –
    – Formulation of relevant questions for further investigation.
    – Ability to identify and judge trends.
    – Ability to identify co-variation between variables.
    – Ability to use data visualization for presetting and communicating results.


  • Consumer-Facing Visualizations (DATA VISUALIZATION):       

    Data visualization is the culmination of every data analysis technique into concrete and actionable insights. One of the most important tools of data visualization has been dashboards. They are universally useful but are preferred by business users as they give them a visual representation of data. Also, dashboards help in team collaboration as many data scientists can share their input collectively on it. They are also highly interactive; a must-have for business users. Business analysis is meant for strategic decision-making rather than technical details. You will discover that the output of a data science project is delivered to the client in a dashboard.Data Science Project – Data Visualization with DashboardsA Python user can take help from the Bokehand Plotly But, if you prefer R as your data science language than you can go through RStudio’s Shiny package.

    Your project must be able to showcase the following skills:
    – Identifying metrics specific to the customer’s requirement.
    – Developing useful and relevant features.
    – An understandable layout for easy and quick scanning,
    – Generating an optimum refresh rate.
    – Preparing reports on automated actions.


  • MACHINE LEARNING Projects (Predictive Analytics):

    Needless to say, machine learning can surely be an added advantage for your portfolio. When we say machine learning project, we do not expect you to dive straight into deep learning with complex algorithms. You can always begin with simple algorithms which are equally useful and easy to explain. For example, you can begin with basics like linear regression and logistic regression. Also, ensure that you choose projects which have some real-life implication in the business world. You can take basic projects like fraud detection and loan default.

    Data Science Projects – Study of Logistic & Linear Aggression Model
    An Example of Logistic and Linear Regression
    if you want to learn more about Machine Learning Algorithm. You can start from here:-
    Regression vs Classification in Machine Learning

    For those working with Python, we recommend Scikit-learn For those who are more comfortable with R, we recommend the Caret package.

    Your project should be able to convey the following –
    – Your reasons for choosing the specific algorithm and model.
    – Data splitting into test-set including k-fold cross-validation.
    – Feature engineering and selection.
    – Hyper parameter tuning

    Also, it is important to understand your target audience.

    You must choose your communication toll based on the audience; a tool for an ML expert and business manager will be different.


    Your project should be able to display the following skills:
    – Ability to understand the audience.
    – Ability to present with visualization.
    – Ability to make simple and concise slides.
    – Ability to maintain the flow of the presentation.
    – Ability to connect the result with a business decision.

    Also, let us give you an additional tip – document your project in Jupyter Notebooks or RMarkdown and the covert them to websites for your GitHUb profile.

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