Through this Blog, we will read about what is data science, why it is such a buzzword these days, what makes data science such an effective and a hot technology to look forward to, what is it like to be a data scientist, what do you need to achieve to be a data scientist. You will also be made familiar about the applications, advantages, disadvantages, examples, real-life use cases, differences between machine learning and artificial intelligence vs neural networks vs deep learning vs prediction analysis.
We will also be reading about the various frameworks and libraries which are in very popular demand these days such as Numpy which stands for numerical python, Pandas for data frames, Scikit learn for cross-validation techniques and other model fitting techniques, seaborn for analysis, heatmaps, Tensorflow, etc. Data science is probably the most unexplored territory today and the scope to learn and create and do something out of the box is way too much in this technology and field of sciences and mathematics.
In this section we are going to read about the basics of data analytics and how it is helpful in making our lives more meaningful and helpful. Growing massive data with every passing day needs to be managed somehow and by doing analytics on top of it we ensure that it is being rightly used for generating insights for an organization. The topics include offline marketing campaigns, Scientific libraries such as pandas, Numpy, Scipy, matplot lib, sci kit learn, Matlab, Pentaho, Data warehousing, data mart, TensorFlow, Caffe, Theano, Data exploration, Spark, Hadoop, Big data, Talend vs informatica PowerCenter, etc. big data as well as ETL tools.
In this article of the article we will be studying about the career prospects with respect to the data analytics. This includes the career in google adwords, data scientist, as a business analyst, data visualization, big data, data supply, statistician, Teradata career, deep learnings, splunk, devops, azure, etc. This has been a new field and if you are planning to make your career in this domain then you will be among the elite class of engineers, analysts, etc. You will find topics related to all analytics in this article.
In these article you will read about the interview questions and answers on all the technologies which are prevalent today and are widely used in market. This includes all the software related legacy technologies as well as new analytical technologies. This article focuses on Cassandra, Cognos, Hbase, ansible, hadoop cluster, business intelligence, tableau, data modeling, splunk, Hadoop admin, Elastic Search, Matlab, Power BI, Minitab, Statistics, Hive, Deep learning interview questions and answers, etc. This post will provide you the information regarding all the interview questions and their corresponding answers related to data science and should be very helpful if you are planning to pursue your career in any of these technologies.
In this section you will learn about the different topics in Data commands. How it actually works? Like Functions of data commands in R, (ANOVA) Analysis of Variance is used to compare the mean value of different groups. It also has topics like GLM in R which means Generalized linear models it is a subject of linear regressions models and support non-normal distribution effectively.
This section of Blog discusses you will read about the most recently used buzzword of the decade i.e. cloud computing. As you are aware this technology has been among the niche technologies where all the data is being transformed on the cloud systems from on-prems. This includes different types of cloud computing systems such as IaaS, PaaS, SaaS, etc. Here we will be reading about cloud computing concepts such as Azure, AWS, GCP, Salesforce, their advantages and disadvantages, certification and its benefits, differences among these technologies, services, Careers in these technologies, Digital ocean and examples related to them.
In this blogs of the article you will learn about the most prominent and hot industry after data science in the market today when it comes about analytics i.e. Big data. The word big data is derived keeping in mind the increasing levels of data every data and lack of efficient mechanisms to handle them. In this article you will learn about batch and real time processing, Streaming , pub sub technologies, messaging systems, hadoop, MapReduce, Hive, Spark, oozie, Sqoop, NoSQl databases, EDW, Python and other data warehouses, metadata, Impala, etc. All this is relevant as they are the tools responsible to handle the vast amount of data.
In these articles you will read about the various business analytics techniques and tools. As you might be aware that the big data and ETL is only helpful in generating the data and creating a meaningful set of information but the vast majority of the data is considered a waste if appropriate business analytics is not applied to it. This includes Google Analytics, Piwik, Adobe Analytics, predictive analysis, universal analytics, customer analytics, interview questions, their advantages and disadvantages, certification and its benefits, differences among these technologies, services, careers in these technologies as well as examples in these technologies.
This section you will come to know about the various kinds of data mining techniques and tools which are helpful in determining the right set of information out of the metadata generated from these data mining tools. This includes tools such as Web mining, text mining, data mining, image processing, Natural language processing, how text mining works, techniques for best results, text analytics, predictive analytics, clustering in data mining,statistics, advantages, uses and applications, examples, business intelligence, big data, data science, etc. Data mining is essential because it becomes necessary to extract the useful data from a set of raw piece of data.
In this blog category you will read about the various data visualization techniques such as Tableau vs Spotfire, power BI, Data science, Data visualization, treemap. Uses, Server and their applications, date functions of tableu, Microsoft power BI, Visual analytics, QlikView, tableau commands, Looker, Domo, etc. Data visualization forms an essential component in the field of analytics as this data needs to be understood, worked upon and further predictions and analysis can only be done on top of that once the base data is visualized and hence becomes an urgency to make use of these tools.
Here comes the most interesting and widely popular topic of today i.e. machine learning. In this section you will read about what is machine learning, why is it helpful, how to practice it, how is it beneficial for the businesses, advantages, scope, disadvantages, applications, etc. Concepts related to machine learning such as supervised, modeling, unsupervised and reinforcement kinds will also be explained. Other topics such as the use of Deep learning, mining, Statistics, NLP i.e. Natural language processing, algorithms such as k means, K nearest neighbors, random forest, decision trees, classification, regression, frameworks and libraries such as SciPy, scikit learn, Numpy, etc. will also be discussed in this blogs.
In these resources you will read about the importance of statistics and how it is helpful in the field of analytics and data science. By making use of statistics one can easily predict and know the underlying pattern of the data and therefore it becomes essential to make use of different mathematical statistical models to ensure that the right set of data is being looked at. You will read about SPSS vs Stata, Mulesoft, Talend, Excel, Statistical analysis software, SAS, Standard deviation vs Mean, Median, Mode, Manova, cluster analysis, Minitab, regression and classification, R, predictive analysis, clustering techniques, etc.