Starting with Data Science

Data driven company is the base of the pyramid which defined key performance metrics based on which you need to measure the health for success of your company.

You need to track the success of your company like that of Google Analytic. You need to store the database somewhere from where you can go to BI.

This can examine what your company is doing at certain point of time. You need to keep track of change in demand.

Why and How the work is being done.

We can predict the future after analyzing the past. Recommendation engines are used to engage more customers as per their liking.

Data Analytic, machine learning tools are used.

There are four different types of packages which are required for data analytic:
  1. Data Extraction
  2. Data Visualization
  3. Time Series
  4. Machine Learning
RGoogleAnalytics is the primary package used to extract data from Google Analytic. 
Ggplot2 is the basic package used for Data Visualization. 

Comments

Post a Comment

Popular posts from this blog

Different Fields for Data Science

Image
Data science , also known as   data-driven science , is an interdisciplinary field about scientific methods, processes and systems to extract   knowledge   or insights from   data   in various forms, either structured or unstructured,similar to   Knowledge Discovery in Databases   (KDD). Data science is a " concept to unify statistics, data analysis and their related methods " in order to "understand and analyze actual phenomena" with data.   It employs techniques and theories drawn from many fields within the broad areas of   mathematics,   statistics,   information science, and   computer science, in particular from the subdomains of   machine learning,   classification,   cluster analysis,   data mining,   databases, and   visualization. Turing award   winner   Jim Gray   imagined data science as a " fourth paradigm " of science (empirical,   theoretical, computationa...
Read more

Different types of clustering for textual documents

Clustering Model 1: Nearest Neighbour Algorithms: KD-Trees and Locality Sensitive Hashing Core Machine Learning Concepts: Distance Metrics and Approximation Algorithms Problem Domain: Finding similar documents Nearest neighbour: Find distance between all other documents and query document. Retrieve the document which is closest Critical Component Document Representation Computing Distances Scaling to large dataset: KD Trees - Not appropriate for very high dimensional documents Thus, Locality sensitive hashing is used for approximate nearest neighbour search. Clustering Model 2: Capturing uncertainty in clustering: Mixture Models Any document can be related to t i topic by x%. Can learn users topic preferences Making soft assignments to clusters -Expectation Maximization Clustering Model 3: Latent dirichlet allocation Mixed membership Probability of words in vocabulary Unsupervised learning task
Read more

Significance of Woman in Data Science

Image
Research scientists, regardless of gender, are doing great in this field. This has been evidenced by a great conference " Global Women in Data Science " hosted by Stanford University (livestream) for 75+ locations all over the world on 3rd February 2017. Many talented academic researchers and practitioners have successful connected to make their dreams and goals burst into reality. It has been sponsored by Microsoft, Google, WalmartLabs, SAP and many more A Grade firms.  Fig 1: Global Women in Data Science Another great initiative, Women in Machine Learning   has been hosted on Facebook. Many female potential researchers join hands for research in machine learning. Many of them have achieved great milestones and many are still on the way.  The world is full of talented, successful and fantastic female speakers Women in Machine Learning and Data science are often un-noticable by traditional recruiters and we can turn it down. Another initiative at Meet...
Read more