Blog posts on Data Science, Machine Learning, Data Mining, Artificial Intelligence, Spark Machine Learning

Monday, February 3, 2014

Data Analysis Steps

After going through the overview of tools & technologies needed to become a Data scientist in my previous blog post, in this post, we shall understand how to tackle a data analysis problem.
Any data analysis project starts with identifying a business problem where historical data exists. A business problem can be anything which can include prediction problems, analyzing customer behavior, identifying new patterns from past events, building recommendation engines etc.

The steps for solving a data analysis problem can be shown as below:

Identify Business Problem:
 “Define Problem statement”
This is the first step of analysis. Business identifies a problem and a problem statement with desired outcome is defined. In this stage, a Data Scientist should understand the problem statement, the domain knowledge of the problem. After thorough understanding of the problem statement, a Hypothesis will be proposed.

 Data Acquisition:
“Identify data sources”
As a second step, all the data sources related to the problem statement will be identified and pulled into a central repository. The data sources can vary from SQL databases to text files to csv files to online data. If the data size is large we may use Hadoop to pull, store & pre-process the data.

Process/Clean Data:
 “The accuracy of the results of analysis depends on the quality of data” 
Data Clean step is considered to be one of the very important phases in Data analysis. The accuracy of the analysis depends on the quality of data.
Few approaches:
  • Formatting the data as per the data analytical tools we use.
  • Missing data handling
  • Data Transformations like normalizing the data Identifying outliers & handling etc.
Exploratory Analysis:
 “Embrace the data visually before diving further”
The objective of this step is to understand the main characteristics of the data. This analysis is generally done using visualizing tools. Performing an Exploratory analysis helps us:
  • to understand causes of an observed event
  • to understand the nature of the data we are dealing with
  • assess assumptions on which our analysis will be based
  • to identify the key features in the data needed for the analysis
Graphical Techniques:Scatter plots, box plots, histograms
Quantitative techniques: Mean, median, Mode, Standard deviation
Model Generation & Validation:
“Select-Train-Evaluate” 
This step involves extracting features from the data and feeding them into the machine learning algorithms to build a model. Model is the solution proposed for the problem statement. This step involves: Model selection, model training and model evaluation.
Model selection: Based on the type of business problem we are dealing, a model will be built. For example,if the objective of the analysis is to predict a future event, we need to build a Regression model for prediction.
Model Training: After selecting the Model for the analysis, the entire dataset is divided into 2 parts – Training data & Test Data. 3/4th of the entire data will be fed as input to the Model Algorithms.
Model Evaluation: Once the model is built. The next step is to test the model & validate it. The data used for testing the model is the remaining 1/3rd of the dataset in the previous step.
Visualize Results:
 "Show the results visually" 
This is the final step of Data analysis where the results of the model & problem solved will be presented generally in visual plots/graphs.
Few visualizing tools: d3.js, ggplot2, tableau.

Please go through the tools/technologies , skill set required to learn Data Analysis here

12 comments:

  1. With the base of endeavors however limit of conceptualizing, the reality of the business is changed. It goes with the assessment of the on-going tasks and profitability.data science course in pune

    ReplyDelete
  2. Well, The information which you posted here is very helpful & it is very useful for the needy like me.., Wonderful information you posted here. Thank you so much for helping me out to find the Data science course in Mumbai
    Organisations and introducing reputed stalwarts in the industry dealing with data analyzing & assorting it in a structured and precise manner. Keep up the good work. Looking forward to view more from you.

    ReplyDelete
  3. Attend The Data Science Courses in Bangalore From ExcelR. Practical Data Science Courses in Bangalore Sessions With Assured Placement Support From Experienced Faculty. ExcelR Offers The Data Science Courses in Bangalore.
    ExcelR Data Science Course Bangalore

    ReplyDelete
  4. Such a very useful article. I have learn some new information.thanks for sharing.
    data scientist course in mumbai

    ReplyDelete
  5. Nice blog Thank you very much for the information you shared.
    data science

    ReplyDelete
  6. I was blown out after viewing the article which you have shared over here. So I just wanted to express my opinion on Data Analytics, as this is best trending medium to promote or to circulate the updates, happenings, knowledge sharing.. Aspirants & professionals are keeping a close eye on Data Analytics Course in Mumbaito equip it as their primary skill.

    ReplyDelete
  7. Such a very useful Blog. Very interesting to read this article. I have learn some new information.thanks for sharing. know more about

    ReplyDelete
  8. I am really enjoying reading your well written articles. It looks like you spend a lot of effort and time on your blog. I have bookmarked it and I am looking forward to reading new articles. Keep up the good work.
    Click here

    ReplyDelete
  9. I have to search sites with relevant information on given topic and provide them to teacher our opinion and the article.
    ExcelR data science

    ReplyDelete
  10. Awesome blog. I enjoyed reading your articles. This is truly a great read for me. I have bookmarked it and I am looking forward to reading new articles. Keep up the good work!
    ExcelR data analytics

    ReplyDelete
  11. Such a very useful article. Very interesting to read this article.I would like to thank you for the efforts you had made for writing this awesome article.
    ExcelR Business Analytics Course

    ReplyDelete