The tools and techniques used to make the extraction of insights easier in both Data Analytics and Data Mining is called Data Science, Understanding about : Data Analytics Data Analysis Data Mining Data Science Machine Learning Predictive Analytics Big Data Business intelligence Data warehousing Business Analytics Hadoop IOT DevOps Start with the following : Learn TOOLS LIKE SQL, NoSQL , ETL, BI. Hadoop is a group of tools like Apache Pig, Hive, Flume, MapReduce etc
Those who are willing to work in the field of data science and play roles like Data Analyst, Data Engineer,Data Scientist, Data Architect should learn this course
Data Science is the study of the methods of analyzing data, ways of storing it, and ways of presenting it. Often it is used to describe cross field studies of managing, storing, and analyzing data combining computer science, statistics, data storage, and cognition. For those who build or manage data systems and analysis and then use their technical and business or specific subject knowledge to bridge the technical and business sides of a company or scientific project
B,E , MTECH , MCA, Software Engineers with Experience in Programming Logic and Coding
PC or Laptop and Internet Connection
- Ability to architect large scale systems.
- High attention to detail including precise and effective customer communications
- solve business problems by finding patterns and insights within structured and unstructured data.
- Interpret data and analyze output using statistical techniques.
- Design experiments, test hypothesis and build models.
- Develop algorithms to extract information from data mining
- predictive modeling / Machine learning techniques skills
- Learn how to visualize data through graphing/ charting/ information display skills
Build Excellent data structures & algorithms skills
Experience of working on massively large scale data systems •
Experience in leverage user data for behavioral targeting & ad-relevance
Experience of building products that are powered by data, insights and visualisation tools.
Ability to architect large scale systems.
work with structured and unstructured data.
Interpret data and analyze results using statistical techniques.
Design experiments, test hypothesis and build models.
Develop algorithms to extract information from Advanced data mining
Apply predictive modelling (especially Machine learning techniques) skills
Data Science in Depth Course detail is as mentioned below
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Newswhip/ Parsely/ Flurry/ Optimizely/ Google Analytics 360/ Google DFP Premium/ Google Big Data Query/ Comscore/ first party cookies
Create actionable insights from content and audience data
Hands on experience in driving insights at a product and channel level starting from Google Analytics/Mixpanel/Fire base/ Omniture and similar web tool. data analytics for App Marketing, SEM, SEO, B2B lead generation, Email Marketing etc
Specific type of audiences based on profile and behavior
Basics of Structured query Langauge - SQL . Learn query languages such as SQL. Experience with extracting data, SQL queries, and producing analytics
IDLE, Pycharm, IronPython, Notebook, Aneconda, PIp, Spyder IDE, PyLint, PyChecker, Notebooks such as Zeppelin, Jupiter, Databricks, etc
KNMIE, Data Applied, Zeptospace, DevInfo, KNitr, pytz and babel, rpy2, Aneconda, Cython, D3.js, Python – Web scraping Tools, DataWrapper, Octave
Capstone, seaborn, pylab, Mathplotlib, ahiny, rcharts, GoogleVIS, Graph DB's Practical experience with data visualisation tools, such as D3.js, GGplot, etc
understanding of text mining , Search technologies and NLP techniques. NLP and contextual analysis, strategic recommendations
ROOT is an object-oriented framework for data analysis.- Numby, scipy, sumpy, pandas, quandl, scilit, LAPACK, LIBSVM, pyTables, RADIS, ROOT. Indepth use of common data science toolkits, such as R, Weka, NumPy, MatLab, etc.
Statistical analysis of flow data using Python and Redis , Statistica , scipy-stats, Akka, ANOVA, statistical inference
Statistical concepts and calculations - correlation, regression analysis, Trend analysis, Descriptive analysis
Work experience in statistics skills, such as distributions, statistical testing and regression
Knowledge and expertise with typical statistical packages and libraries - R, NLTK, NumPy, SciPy
Analytical and quantitative skills to develop project business cases and deciding the right priority.
A business case in which the problem and the goal statements indicated and converts it into a statement of business importance.
Write business case including problem statement and goal statements, project solving and high priority problems of the organization
data was brought together to discover previously unknown correlations, trends and anomalies
purpose of data mining is to mine the rule and knowledge using large set .
The knowledge is extracted and represented like rules. These rules can be used to predict the future data. Data mining is to discover patterns in large sets of data
Excellent data structures & algorithms skills working on massively large scale data systems
Sound knowledge to leverage user data for behavioral targeting relevent to ad. Exposure to Building products that are powered by data & insights
Modeling tools (ERWin, Power Designer, ERStudio)
Customer Modelling and analytics, driving enhancements, advertising/ behavioral/ marketing/ transactional analytics specialist in identifying, developing and implementing the appropriate statistical techniques, algorithms and data mining analysis to create new, scalable solutions that addresses business challenges.Tools : python, Tableau, Octave, Matlab, D3.js, dyGraphs, DataWrapper, Javascript, MongoDB, SQL, SQL, Excel
Ability to find valuable insights out of cluttered and unorganized data with expert Data Analysis, Customer Analytics, Marketing Analytics
Using MongoDB, Cassendra, Hbase, Databases, Mallet, PyBrain , PyTables
Practical experience in Amazon Redshift, petabyte-scale data warehouse service in the cloud. Virtualized environments such as ESX, Xen, KVM,AWS, EC2 NAS or SAN Distributed file system
Working on Talend ETL Tools<, SAS, SPSS, BusinessObjects, Cognos, Qlikview, MicroStrategy, Pentaho, etc
Analytcal and ETL Tools and Visualization Platforms – Tableau, D3 Web Scraping - Visualization and Reporting Tools like Qlikview and Micro Strategy and tableau - working with data services team to do ETLs (extract, transform, and loading) Good experience in working with any ETL tools such as Informatica, Talend , Pentaho and/or open source tools popular data analytics tools include KNIME, Data Applied, R, DevInfo and Zeptospace
Data warehousing The technology associated with storing data to allow for either reporting or transactions typically.
Spotfire, Open Source BIRT , Informatica, Tableau, jasper, qlikview, Lattice system , ggplot2 system
reporting and dashboards for marketing and traffic as well as funnels across multiple products
Devise ways to accurately measure the impact of each and every marketing/product initiative. • Report and analyse
website traffic data by channels, cities, page type, devices etc. • Devise models that identify reasons for the dips and increase in website traffic/transactions. • Estimate the potential impact of planned marketing initiatives
Predictive Analytics Creating a quantitative model that allows an outcome to be predicted based on as much historical information as can be gathered. In this input data, there will be multiple variables to consider, some of which may be significant and others less significant in determining the outcome. The predictive model determines what signals in the data can be used to make an accurate prediction. The models become useful if there are certain variables than can be changed that will increase chances of a desired outcome.
Data mining is more about exploring data, whereas machine learning is focused on determining precise functionality and thereby studying this data.
Sparkling-water
– Logistic Regression, Naïve Bayes, CART, Decision trees, CHAID, Random forest, MaxEnt, Neural Networks, Support Vector Machines, Reliability models, Markov Models, Stochastic models, Bayesian Modeling, Classification Models, Cluster Analysis, Neural Network, Non-parametric Methods, Multivariate Statistics Machine Learning - Bayesian, Decision Trees and Neural Networks.Predictive Analytics, Data Visualization, Product Analytics, Data Mining & Business Intelligence, Web Analytics, Logistic Regression, Clustering, Decision Tree etc
Machine learning is the set of tools processes and algorithms to construct the learning function. The function is then expected to generalize the learning to real world facts and provide inferences, predictions etc. Machine learning describes a class of technologies that enable computers to detect patterns and determine contextual meaning. The term usually applies to autonomic approaches where computers do not require human intervention.
Machine Learning: this is one of the tools used by data scientist, where a model is created that mathematically describes a certain process and its outcomes, then the model provides recommendations and monitors the results once those recommendations are implemented and uses the results to improve the model
scikit-learn, Spark.
understanding of machine learning techniques and algorithms, such as Clustering, k-NN, Naive Bayes, SVM, Decision Forests, etc. • Experience with common data science toolkits, such as R, Spark MLib, TensorFlow, MatLab, etc. Excellence in at least one of these is highly desirable
Machine Learning - Bayesian, Decision Trees and Neural Networks Expertise in analytical techniques such as Supervised ML (Linear Regression, Logistic Regression, Cart, Chaid, Random Forest, KNN, SVM, etc), Unsupervised ML (K-Means, Distance metrics, etc), Forecasting (Exponential Smoothening, ARIMA, etc), Linear/Non-linear Optimization
Web Application Development using Django, MangoDB & Flask
Introduction - Hadoop Technologies
Big data is only data but it means a lot of data. It might be so big that you might not be able to use traditional computer tools and need parallel processing, distributed computing and even cloud computing. Hadoop Streaming API
Working on Machine Learning applications and tools such as Mahout, Mallet, PyBrain
A nonparametric test is a hypothesis test that does not require the population's distribution to be characterized by certain parameters.
Non parametric tests to modify the hypotheses
non-parametric covers techniques with varying structure of a model .
Practical Application appying techniques
Medical, Pharmacy, Online Shopping, CRM etc
• We Focus on more generic and open source solutions
• Programs are taught by Data experts
• Vast experience in Application and Data Management industry
• Program tailored to participants needs
• Arrange Job Interviews
• Trained Many Candidates