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Fundamental Steps For a Data Analytics Project Plan
  • Last Updated : 03 Nov, 2020

It always seems hard to know where to start your data analytics project. At beginning of the projects, you always face some questions like What are the goals of the project? How to get familiar with the Data? What are the problems you’re trying to solve? What can be the possible solution? Which skills are required? How will you evaluate your model and most important where to begin? 

Well! The creation of strong planning and process is an essential beginning step to kick start your project initiative. We should always follow a well-defined workflow to build a data model. In this article, we come up with some essential steps to help you plan a data science project successfully. 

Fundamental Steps of a Data Analytics Project Plan

We break down the entire data science framework, taking you through each step of the project life cycle while discussing what are the key skills and requirements of it.

1. Find  an Interesting Topic

Your project must be the answer to a clear organizational need so you should always concentrate on the overall scope and objective of the topic. Many problems can be solved by analyzing data and improving the data but you should choose a topic that motivates and fascinates you. For instance, if you are interested in Healthcare Analytics, there are many topics you can try-Lung cancer classification based on gene expression levels, EEG based emotion recognition in music listening, Breast cancer detection using anomaly classification.



2. Obtain and Understand Data

There are many online data sources where you can get free data sets to use in your project. Some amazing data repositories- Kaggle, Google Cloud Public Datasets, Data.gov, and websites containing academic papers with datasets. Websites such as Facebook and Twitter allow users to connect to their web servers and access their data. You can use their Web API to crawl their data. Sometimes data comes in a certain format so, it’s best to become familiar with some of the forms that data might take, as well as how to view and manipulate these forms. Here are some of them: Flat Files (csv, tsv), HTML, XML, JSON, Relational Databases, Non-Relational Databases, APIs. After obtaining data the next step is exploring and cleaning data. When going through the data sets, look for missing data, duplicate data, different spelling errors, or even the data that doesn’t make sense logically. To organize your data you can use different tools –R, Python, Tableau, Spark, etc. 

3. Data Preparation

To perform any analytical activity on any data it needs to be in a structured format. This step is known as Data Cleaning or Data Wrangling. You have to verify if data types in data are compatible or not? Are there missing values or outliers? Are there any naturally occurring discrepancies or errors that should be corrected before fitting the data into a model? Do you need to create dummy variables for categorical variables? Will you need all the variables in the data set? For analyzing data to summarize their main characteristics Exploratory Data Analysis plays an important role. It identifies outliers, patterns, and anomalies in the data that could help you in building the model.

4. Data Modelling

In this step, you will begin building models to test your data. It seems the most interesting stage but remembers before this step you spend sufficient time and techniques in prior steps. You can use different modeling methods to determine which is more suitable for your data. The very essential thing to do in modeling data is to reduce the dimensionality of your data set. You can use regression for predicting future values, and classification to identify, and clustering to group values. For model performance measurement, precision, recall, F1-score can be used in classification.

5. Model Evaluation

Once you have crafted your model you need to evaluate the model thoroughly. In this stage you have to determine if your model is working properly, did you get the desired outcome also if it meets the business requirements. Always ensure that data is properly handled and interpreted. There are two methods of evaluating models in data analysis, Hold Out and Cross-Validation. They help to find the best model.

6. Deployment and Visualization 

This is the final and the most crucial step of completing your data analytics project.  After setting a model that performs well you can deploy the model for different applications and in the business market. This phase examines how well the model can withstand in the external environment. To explain your findings to the client you can use different interactive visualization tools. Data Visualization is a graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide a quick and effective way to communicate and illustrate your conclusions. 

To perform the tasks above, you will need certain technical skills and tools like Python or R. If you are using Python, you need to know how to use Numpy, Matplotlib, Sci-Kit learn, and Pandas. If you are using R, you should know GGplot2,CARET, or data exploration. For handling bigger data sets you are required to have skills in Hadoop, Spark. Soft Skills like communication and writing skills will effectively help you throughout the project. You should be familiar with statistical tests, distributions, maximum likelihood estimators, etc. More important is to understand the broad strokes and understand when it is appropriate to use different techniques. After completing your project you should always make sure that it remains useful and accurate. You need to constantly re-evaluate, retrain it, and develop new features. 

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