Follow up on our discussion regarding what you should have worked on before graduation: Discrete mathematics Computer science theory (automata theory, lexical analysis, etc) Know a programming language very well (all the famous packages in that language including the data structure ones and the string manipulation ones too beside the basic language function) Beside data structure, most major algorithms (sorting, tracing, etc.) You need to know web development stack if you will be doing that in the future. These are the things I believe are important for you. Once you master these skills, you need to put them in action in form of real-world projects. I said real project because remember the environment and deadlines are our motivation. This is a comprehensive list of all the topics and subtopics in computer science. Check what's missing. You don't to be perfect in all of them, but you need to master at least one: https://en.wikipedia.org/wiki/Outline_of_computer_scien...
When presented with more options, choosing among big data tools can seem confusing. This blog explains the benefits and uses of the various option paths available to help guide choices for your next big data project. Spark , Storm , or Kafka : It is an in-memory data grid that provides real-time data access to applications that are critical to the revenue stream of the business. MangoDB : It’s massively parallel processing style of data management makes it an excellent choice for analytics. Hadoop : is your research and development arm. As the landing spot for all data, and powered by a powerful SQL query engine, you can explore all data to identify new insights and opportunities you can later operationalize with MPP or in-memory. Questions you need to consider when choosing: When do I need it? Now? Later? What do I want to do with it? Singular event processing? (includes some analytics), Transactions? Exploratory analytics? ...
This week, I was reading a book by Jonah Burger (The Catalyst: How to Change Anyone's Mind) which is one of his great collections of social influence topics. In the book, he mentions that everyone has something they want to change. Employees want to change their bosses' minds, and leaders want to transform organizations. Salespeople want to win new clients, and startups want to revolutionize industries. Parents want to change their children's behavior, and political canvassers want to sway voters. But change is hard. We pressure and coax and cajole, and often nothing moves. Could there be a better way? Whether you are trying to convince a client, change an organization, disrupt a whole industry or just get someone to adopt a puppy, the same rules apply: Reduce reactance: Allow an agency, make them feel they made the decision. Ease endowment: Bring the cost of inaction to the service, if you don't upgrade, we won't support Shrink distance: Uber to rid...
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