I am very much confused in understanding machine learning, data analysis, data mining, data science.
Where exactly they differ?
What is the difference between machine learning, data analysis, data mining, data science and AI?
Here is a brief description of each of these terms:

Data Science: Refers to the umbrella of techniques where you are trying to extract information and insights from data. This includes MIS reporting on the lowest level to building predictive models on the higher level.

Data Mining: refers to the science of collecting all the past data and then searching for patterns in this data. You look for consistent patterns and / or relationships between variables. Once you find these insights, you validate the findings by applying the detected patterns to new subsets of data. The ultimate goal of data mining is prediction  and predictive data mining is the most common type of data mining and one that has the most direct business applications.

Data Analysis: This is a loosely used term. People running reporting also say that they are analysing data and so do predictive modelers. I would just take this as any attempt to make sense of data can be called as data analysis.

Machine learning  is the science of creating algorithms and program which learn on their own. Once designed, they do not need a human to become better. Some of the common applications of machine learning include following: Web Search, spam filters, recommender systems, ad placement, credit scoring, fraud detection, stock trading, computer vision and drug design. An easy way to understand is this  it is humanly impossible to create models for every possible search or spam, so you make the machine intelligent enough to learn by itself. When you automate the later part of data mining  it is known as machine learning.
Hope this helps a bit.
Regards,
Kunal
Thanks Kunal,
Things are almost clear to me now. I am exploring this field as I find it interesting.
@kunal Thanks for the glimpse of the terms!! I am little confused at the terms data mining( machine learning included) where we are building a model using historical data and the data analysis using the statistics where we are coming up with the solution for a problem using Statistics.
To be more precise my question: The learning steps and topics which has to be covered in data analysis, its uses from business point of view and the same for the machine learning ( as data mining is included in it).
It would be great help if the topics and understanding of Statistics would be given stress in details.
Thanks,
neel
Fantastically written. To be honest I myself was not very clear on these terms & I’ve been in the field for 5 years now. Sometimes we tend to do complicated stuff & get tangled into it without knowing exactly what is what. Thanks a lot Kunal.
This site http://www.kdnuggets.com/2014/06/datascienceskillsbusinessproblems.html and below diagram would be able to answer your question in detail.
thank u very much kunal.
Thanks a lot for this article.Really helpful
Great explanation @kunal.
Here is another article I found explaining difference of job roles in the field of datascience.
Cheers
Shravan
Data Science, Analytics, Mining and Machine Learning are growing at an astronomical rates in a companies
Data Science : It is a concept used to tackle problems and includes data cleansing, prepration and data analysis.
Data Analysts : It is usually a person who can do basic descriptive statistics , visualize the data and communicate data points for the conculsion.
hey must have basic understanding of statistics and have a perfect sense of databases. It has the ability to create views and perception to visualize the data.
Machine Learning : It can be defined as the process of using algorithm to extract the data, learn from it and forecast the future trends. A good example of Machine Learning implementation is Facebook. It is the best social platform of every user which gathered behaviorial information of Machine Learning Algorithms.
Data Mining : It is the process of finding and extracting useful information out of the large datasets. Underlying patterns in datasets are explored using data mining.