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Updated Aug 2018The following sections will outline five skills that will help you further a career as a Data Analyst:Data Exploration via Excel/Google SheetsData Extraction with SQLData Visualization via TableauData Automation via PythonData Analysis/Science with Python + Stat librariesWho this is for - College students, new graduates, career changers, and new analysts will probably benefit most from this article. It assumes you have minimal analytics, programming, or work experience. This article should help you build a foundation so you can begin or further a career in data analytics.Who I am - I’m a self-taught analyst who has worked at various companies (Netflix, CNET, Zynga) in a variety of analytical roles (Marketing, Finance, Social, Growth) for over a decade.Two notes before proceeding:This article will not outline how to become a data scientist or data engineer (read more about the differences), which generally require degree(s) in statistics or computer science respectfully.While you can learn these in any order, you’ll probably progress most seamlessly by starting with #1 and #2 before #3–51. Data Exploration via Excel / Google SheetsAt most organizations, Microsoft Excel and/or Google Sheets are the most broadly used data applications. While many tools perform a specific function very well (such as Tableau for visualization), few can enable most lightweight data tasks as easily as a spreadsheet. Not only are Gsheets/Excel the Swiss Army knives of data exploration, they also have a relatively shallow learning curve, which make either a great tool to learn first. If you’re dead-set on other analysts skills, don’t spend too much time here--but don’t make the mistake of not becoming familiar with a spreadsheet program either. Many data questions can be answered and communicated with a spreadsheet faster than with other technologies.Start by learning the following:FormulasGeneral Formulas. Once you’ve downloaded the data, see if you can enhance it with some formulas. The IF statement, boolean logic (AND, OR), and VLOOKUP functions are the most common formulas used across spreadsheets. Afterward, graduate to learning text-based formulas like MID, LEFT/RIGHT, SUBSTITUTE, TRIM. Experiment with the date formulas--such as converting a date (in any format) to the components of a date (year, month, day).Formula References. You should know the difference between an absolute and a relative reference as well as how to input either via editing a formula using the keyboard (F2) as well as toggling either (F4) via the keyboard.Aggregation Formulas. These formulas help you find conditional summary level statistics: SUMIF(s) , COUNTIF(s), and SUMPRODUCT, which are good to learn for reporting purposesInterested in learning more formulas? See this article.Data Filter. The data filter is a key feature which helps end users, sort, filter, and understand a sample from a large data set. Memorize the keyboard shortcut for creating one--you’ll use this often.Pivot Tables. Pivot tables allow an end users to easily get summary level statistics for a given dataset. Learn how to create a pivot table, and scenarios in which to place fields or metrics in the row, column, filter, or value section. Learn how to create formulas at the pivot table level, and understand how creating them on a pivot table is different than at the data table level. Finally, learn the GETPIVOTDATA function, which is especially useful when creating dashboardsCharting and Pivot Charting. Lean how to create bar, line, scatter, and other charts in Excel. Formatting charts is relatively easy--when you want to change something click on it (or right click), and in general the Excel Ribbon or the right click menu will allow you to modify the look and feel of a chart within the ribbon or or menu.Keyboard Shortcuts. As you begin to get more comfortable, begin mastering the keyboard shortcuts rather than use the mouse. Start by learning the basic shortcuts for tactics like find and replace and paste special. Then move onto to navigating using the keypad. Experiment with selecting rows and columns by using a combination of shift and control. You should eventually learn how to add rows/columns, hide rows/columns, delete rows/columns--all by using the keyboard.Excel Dashboard Design. Learn the Data → Pivot → Presentation pattern, in which one separates the source data from summarized data, and summarized data from the viewable dashboard. This pattern will allow you to easily update a report as more data comes in as well as hide complexity from those who just want to see the most important learnings. How? The first tab contains your data, which you should ideally not change. The second tab contains one or many pivot tables that calculate summary statistics needed for the report. The third tab is a dashboard with one or many visuals or data tables that source data primarily from the second tab (and not from first tab). You’ll present just the third tab to end users, but hide the first and second tabs. When displaying summary level statistics, you’ll likely leverage GETPIVOTDATA—instead of using other summary formulas—will has a faster runtime than the summary formulas. This article explains how to create a dashboard using GETPIVOTDATA such that an end user can select various input options and see a visualization change---Some notes:Excel or Google Sheets? Google Sheets performs best with smaller datasets (<10k rows). It’s also free. Out of the box, Gsheets is also more collaborative, and a good solution if your dataset will be viewed or modified by multiple stakeholders. For larger datasets, spreadsheets with lots of formulas, or the use of esoteric features, Excel is usually the preferred optionDon’t learn Excel VBA. If you’re interested in programming, skip to the Data Programming section and consider Python instead.2. Data Extraction with SQLExcel allows you to slice and dice data, but it assumes you have the data readily available. As you become a more seasoned analyst, you'll find that a better way to get at data is to pull it directly from the source, which often means authoring SQL.The great news about SQL is that unlike a procedural based programming language like Python, SQL is a declarative language. In most cases, instead of writing step-by-step syntax to perform an operation, you describe what you want. As a result, you should be able to learn SQL faster than learning most programming languages.I’m not going to outline all of the flavors of data storage solutions (to start, learn about relational vs non-relational databases) but instead focus on what you’re most likely to encounter--a relational database which supports some flavor of SQL.Start by learning the big six reserved keywords:SELECTFROMWHEREGROUP BYHAVINGORDER BYNext, you’ll want to learn common sql functions, such as the CASE statement, boolean operators (AND, OR, NOT), and IFNULL/COALESCE. Next, learn string functions such as INSTR, SUBSTR, and REPLACE.As you begin to write summary level queries which use the GROUP BY keyword, experiment with the aggregate functions such as SUM, COUNT, MIN, and MAX. Following that, learn how to join to other tables. Know the difference between an inner and outer join.Next, take a break from writing SQL and invest in learning more about how relational databases are structured. Know the difference between a fact and dimension table, understand why database indexes (or partitions) are leveraged, and read about why traditional database adhere to 1st, 2nd, and 3rd normal forms. If someone says they have a high cardinality dataset, a snowflaked schema, or slowly changing dimension--you should know what they mean.As you work with larger datasets, you’ll discover that more involved SQL queries require issuing several SQL queries in sequence. For example, the first query may create a table; the second one will insert data into that table; and the third will extract such data. To get started here, read more about temporary tables. Then you’ll want to learn about column data types as well as how to create traditional database tables and indexes/partitions to support more performant querying.---Some notes:SQL Bolt has a great interactive tutorial to help you learn SQL by doingToptal’s top SQL interview questions can help you get your next job that requires knowing SQLThis section only covered data extraction. As you become more senior, you’ll need to know how to build intermediary tables for analysis, or even construct source tables to store non-temporal data. Read more about SQL DML and DDLIf you’re interested in learning more about dimensional modeling, purchase Kimball’s The Data Warehouse Toolkit, which was originally published in 1996 but still relevant for traditional relational databases today.Try creating your own database locally by downloading and installing mysql or postrgres. Or do so via google cloud.This section only covered relational databases. See this article to learn more about non-relational databases3. Data Visualization via TableauIn the past decade, Tableau has become the leading enterprise tool for visualization. If you’re familiar with pivot tables, you’ll find that creating lightweight visualizations and dashboards with Tableau is relatively easy. To spreadsheet users, Tableau feels like working with an enterprise version of Pivot Tables and Pivot Charts. While keeping your analyses private requires a purchased Tableau Desktop license, Tableau public--which stores any saved analyses to the publicly accessible Tableau portal--is free and a great way to get started learning.Let’s start with Tableau Public--begin by creating an account and downloading the software, and then import a dataset into Tableau. Next, learn more about the panels within the tool. You’ll see the data you’ve added broken up into Dimensions and Measures. Try dragging a given dimension into the columns shelf, and a given measure into the Rows shelf. Tableau will analyze the structure of your data, and automatically generate a visualization (without you selecting one). You can easily change the visualization displayed by changing the type, or by shifting the data between Rows and Columns.After you’ve created a couple of different visualizations across multiple worksheets, create a dashboard. A dashboard can contain one or many views (worksheets) and also allow an end user to manipulate such a view via buttons, filters, and other controls. Start by adding one view to your new Dashboard. Then, add a Filter for a given measure or dimension. Once added, you can change the nature of each filter. For example, you can create a slider to change the range of dates included, or add a radio form to allow an end user to select a given measure. Once you have a functional dashboard, feel free to save it to Tableau Public so you can both view it as an external user would as well as modify it later. For inspiration, see some existing dashboards.From here, there’s a lot more you can do and learn. Tableau’s learning curve quickly steepens as you produce more advanced visualizations and deal with more complex datasets. If you want to continue learning, your best bet is to watch Tableau’s series of free training videos.---Some notes:While Tableau is the current Enterprise visualization market leader, it may not be five years from now. Tableau started as a desktop application and then grew to support web-based reporting, and now many upstarts are producing Tableau-like tools that are 100% browser based (See alternatives to Tableau), responsive by default, and built to work in the cloud as well as integrate with other sources.4. Data Programming via PythonNow you can source data from a database with SQL, manipulate it with a spreadsheet, and publish visualizations via a Tableau dashboard. A next natural step is to learn a programming language. Python is the most utilized programming language in the data community as well as the most common language taught at universities. With it you can achieve a number of data-related tasks such as extracting data from a website, loading said data into a database, and emailing the results of a SQL select statement to a set of stakeholders. If you’re interested in building web application, you could use Python and Flask to create an API as well as create a website leveraging the Flask HTML templating engine Jinja2. Or, you can leverage Python Notebooks for iterative development, the PANDAS library to see the results of a model you’re building as you develop it.The best way to build a strong programming foundation is to start by learning computer science fundamentals. For example, I was introduced to many computer programming concepts via the book Structure and Interpretation of Computer Programs (SICP) at university. Although originally authored in 1979, the book’s concepts are still relevant today and are still leverage today used at UC Berkeley to teach introductory computer science. Once you learn many of the fundamentals, you should be able to apply them to learn any computer programming language. However, learning the fundamentals can take a lot of time--and the content in SICP is academically dense (this review describes it well). Sometimes the better tactic to get started is to learn by doing.I learned python syntax years ago via Learn Python the Hard Way. The online course costs $30 now--and there are plenty of other free alternatives--but when I took the course (at the time it was free), I found it to be one of the better tutorials for learning the Python syntax. If you’re looking for a free option, head to Learn Python or Code Academy.You will have covered python basics when you’re familiar with python variables, control-flow, data structures (lists, dictionaries), classes, inheritance, and encapsulation. A good way to solidify your knowledge is to think of a project you’d like to implement and begin developing—this site has a couple of datasets that you can use to get started.Now that you have the basics down, you’ll want to learn more about how to become a more productive programmer by improving your development environment. The next three sub-sections will cover how to save/share/iterate your work using Github, author Python scripts using Jupyter Notebooks, and make changes to projects using the command line.4a. Learn version control using GitHub/git.GitHub allows you to host, update, document, and share your projects easily online. You’ll soon discover that GitHub will likely be where you end up when you’re discovering new programming libraries. Start by creating a GitHub account (almost all developers have one). Then spend time iterating through the GitHub tutorials, which will outline all of the capabilities of git. Once complete, you should be familiar with how to git clone an existing repository, how to create a new repository, git add files to a commit, prepare a set of changes with git commit, and push changes to a branch via git push. As you invest time in any project, make a habit of committing it to github to ensure that you won’t lose your work. You’ll know that you’re progressing with git once you feel comfortable using the above commands for both managing your own projects as well as cloning other projects to augment your development efforts.4b. Author Python scripts using Jupyter Notebooks As you’re learning Python, you’ll discover that there are multiple ways to author python code. Some developers will use IDEs built specifically for programming such as PyCharm, others elect rich text editors with a focus specifically on coding such as Sublime, and a small minority will edit code exclusively through a shell using VIM. Increasingly, data professionals are gravitating toward using notebooks--specifically Jupyter Notebooks--to author scripts in a web browser for exploration purposes. A key feature within notebooks is the ability to execute code blocks within each notebook rather than all at once, allowing the developer to gradually tweak a data analaysis. Moreover, since the output is in the web browser versus a shell, notebooks can display rich outputs, such as an annotated datatable or timeseries graph beneath the code that generated it. This is incredibly helpful when you’re writing a script to perform a data task and want to see the progress of our script as it executes without leaving the browser.There are a variety of ways to get started with Notebooks. One way is to download Jupyter and run an instance on your local machine. Another option is to use Google’s free version of notebooks or Microsoft Azure Notebooks. I prefer to use notebooks hosted on pythonanywhere, which is the same service I use to host python-based web applications. The free service will let you create your own python apps but you can’t run notebooks--the most affordable tier is $5/month.A good way to learn some of the key value adds of developing with Notebooks is to explore a dataset using the Python Data Analyst library, PANDAS. This site has a great getting started tutorial. Start by importing a dataset and print it out. Learn more about the data-frame storage structure, and then apply functions to it just like you would with another dataset. Filter, sort, group by, and run regressions. Try leveraging seaborn, a statistical visualization library which leverages matplotlib to explore your datasets visually. You’ll quickly discover that the framework allows for repeatable data operations with option for data exploration against a moderate cardinality dataset. Notebooks are often the preferred prototyping interface for data scientists, and thus worth learning how to use if you’re interested in learning more about statistics.4c. The Command Line - using shells and editing with vimIf you’ve read this far, you’ve probably already used a shell, a command-line based user interface for interacting with a computer. You’ve likely used shells to execute python code, download code libraries, and commit changes to git. Knowing how to execute a file, navigate within a shell, and monitor an active process will help you become a stronger data analyst. A great place to learn more about shells is following this interactive tutorial. You know that you’re becoming more proficient with shells when you can easily navigate within a directory, create aliases, change file permissions, search for files and/or contents using grep, and view the head/tail of a file.VIM is a unix-originated command-line text editor which is run in a shell. It’s especially useful when you want to view or edit a file—such as a log or a data output—on a remote server. Initially, you’ll likely find that learning VIM is a bit cumbersome because you primarily interact with the application without a mouse. However, over time you’ll begin to develop the muscle memory needed to toggle between edit-mode, view-mode, and executing commands. A great place to get started with VIM is to go through this interactive tutorial. You’ll know that you’re becoming more comfortable with VIM once you can easily navigate between input and edit mode, go to a row by a number, add or delete a row or character, search and replace text, and easily exit and save files you’ve edited.5. Data Analysis/Science with Python + Stat librariesWhile the goal of this article is not to describe how to be a data scientist--that typically requires a undergraduate and/or graduate level education in statistics--having a solid foundation in statistics will help any analyst make statistically sound inferences from most data sets.One way to get started is to take an online course in descriptive statistics--such as this free one from Udacity--which will teach you how to communicate summarized observations from a sample dataset. While you may be tempted to jump to other hotter industry topics such as machine learning, start with the basics. A solid foundation in descriptive statistics is a prerequisite for machine learning as well as many other statistics applications. After going through Udacity or other tutorials, you should be able to describe various types of distributions, identify skews, and how to describe central tendency, variance, and standard deviation.Next up, graduate to learning inferential statistics (such as Udacity’s free course), which will enable you to draw conclusions by making inferences from a sample (or samples) of a population. Regardless with the learning path you take, you should learn how to develop hypothesis as well as become familiar with tactics for validating such hypothesis using t-tests, understand when to leverage different types of experiments, as well as compute a basic linear regression with one more more dependent variables.The two most popular languages for applying statistics are R and Python. If you’re just getting started, I’d recommend using Python over R. Python is generally considered an easier language to learn. Moreover, Python is typically understood by most teams who build data products. There are more libraries available in Python that can be applied to a wider set of data applications--such as deploying a website or creating an api. This means you can often start an exploratory analysis in Python and easily append a few more libraries to deploy a tool / product leveraging such data, which can reduce the time to release. Finally, data applications continue to gravitate to Python over R as the preferred applied statistics language, so by learning the statistical libraries on Python you’ll be riding this latest adoption trend.Regardless of which language you choose, both Python and R can be executed via Jupyter Notebooks, which allow for more easy visualization and communication as you’re getting started.Next, try learning more about machine learning (Udacity’s free ML course is here). Following any course you should be more familiar with how to differentiate a supervised vs unsupervised learning, understand bayes theorem and how it’s used in ML applications, and outline when decision trees are leveraged. Once you’ve learned the concepts, try cementing your understanding by implementing one of these 8 machine learning projects.Finally, Python has a wealth of free libraries commonly leveraged by data scientists. One way to become more familiar with data scientist tactics are to try experimenting with data science libraries. For example, scikit-learn provides standard algorithms for machine learning applications, and NLTK is a library which can help you process and analysis text using NLP.Wrap UpNow you can write a python script to extract data (#4), store it in a database with SQL (#2), build a model to predict future observations with a python data science library (#5), and share what you learn via a spreadsheet (#1) or a Tableau Dashboard (#5). During that process, you may have committed your code to git, authored in a Jupyter Notebook, and published it on your python-hosted server. Congratulations! You’re well on your way to becoming a data analyst.

Becoming a programmer is a cumulative process that builds up your skills day after day and year after year, and programming can be fun and rewarding (mentally, spiritually and financially). This guide does not promise to give a magically easy way to becoming a programmer, and the ordering of the steps is not sacred, but you'll get a general outline of how to become a programmer in one of the modern programming fields.Step 1 :- Take an introductory course in one (or all of them) of the following disciplines:LogicDiscrete mathematicsProgramming language (take a part into the different programming paradigms, starting from sequential/procedural to object oriented, after functional and logical programming. Preferable Ruby/ Python/Pascal for beginners and after some good understanding go deeper into C++/C#/Java )Step 2 :- Learn database concepts such as tables, views/queries and procedures. You can use any simple database package to do this, such as:MS AccessDB VFox ProParadoxMySQL is a good database to learn because it’s free, commonly used, and databases are commonly accessed with SQL queriesStep 3:- Decide what type of programmer you want to be. Programmers generally fall under one of the following categories:Web Programmer.Desktop application programme (OS) oriented programmer(tied to a single operating system or set of operating systems)Platform-independent programmerDistributed applications programmerLibrary/platform/framework/core programmerSystem programmerKernel programmerDriver programmerCompiler programmerProgramming Scientist.Step 4:- Learn the technologies and programming languages related to your programming field of choice. The following sections break down the tasks for different types of programming.Method 1 :- Web Programming :Know what Web programming entails. Web applications are software components designed to work on top of the internet architecture. This means that the applications are accessed through a web browser software such as Firefox or Internet Explorer. Being built on top of the Internet architecture does not necessarily require an active connection to the internet. It means that Web applications are built on top of standard web technologies such as:HTTPFTPPOP3SMTPTCPIP protocolsHTMLXMLColdFusionASPJSPPHPBrowse many diverse websites to learn about how they usually look. (Right click, then click View Source or press F12.) Look for diversity in the type/content of the website, not the quantity of websites visited. Generally, you will need to visit at least one of each of the following types of websites:Corporate presence sites (commercial corporations, not-for-profit corporate/organizations, governmental organizations)Web indexing engines (search engines, meta search sites, specialized search engines, directories)Data mining sitesPersonal sitesInformational/encyclopedic pages (wikis, data sheets, technical specifications, and manuals listing directories, blogs and journals, news and news agencies sites, yellow pages, etc.)Social sites (social portals, bookmarking sites, note-taking sites)Collaborative sites (this includes other categories mentioned above, such as wikis and blogs)Learn at least one brainstorming technique/method and a software that is used to implement that method. For example: brainstorming diagrams and MS Visio.Get familiar with website structuring. This is creating conceptual web diagrams, site-maps, and navigation structures.Take a crash course on graphics design. Try to learn at least one graphics editing/manipulation software package (optional, but strongly recommended)Learn the basics of the internet infrastructure. This includes getting a basic idea about:Base Web services protocols (HTTP, FTP, SMTP, and POP3 or IMAP4)Web server software (preferably, one for the platform you will be working on mostly)Web browsing software.Email server and client softwareLearn the HTML and CSS languages. You might also want to get the "What You See Is What You Get (WYSIWYG)" software package for editing HTML.Learn XML and XML related technologies, such as XSL and XPath (optional but recommended).Create simple static websites until you are familiar with and comfortable around HTML.Learn a client-side scripting language. Most users learn JavaScript. Some learn VBScript, but this isn't compatible with most browsers.Familiarize yourself with the client-side scripting language you learned. Try to reach your potential using only that language. Only go to the next step after you've at least become familiar with your client-side scripting language.Learn at least one server-side programming language. If you choose to restrict yourself to one server software, learn one of the programming languages supported by that software. If not, learn at least one programming language on each server software.Create a pilot project for yourself after you finish learning the server-side programming language.Obtain your own website and start experimenting online within your own page.Method 2 :- Desktop Application ProgrammingKnow what you're getting into with desktop application programming. Most desktop programmers write code for business solutions, so getting an idea about businesses, their organizational and financial structure will be a big time-saver.Learn about the different computer hardware architectures. An introductory level course in digital circuits design and another in computer architecture is useful; however, some see it as being advanced for a starting point, so reading two or three tutorial articles (such as this one and this one) might suffice. Then you can go back to this step later, after you learn your first programming language.Learn an entry-level (kids') programming language. Don't be shy to learn such a language just because you're older than being called a "kid". An example of these programming languages can be Scratch. These programming languages can ease up the pain in learning your first programming language tremendously. However, this step is optional. It can also be done before the preceding step.Get an introduction to the procedural, object oriented, and functional programming.Take an introductory course in one of the procedural programming languages.No matter what language you choose later to be your language of choice, it will require procedural programming at some level. Also, procedural programming is reported by most programmers to be the easiest to use as a starting point to get the idea of programming in general.Learn at least one advanced modeling technique such as UML or ORM.Start writing some small console or console-like applications. You can make use of common small exercises in programming languages books. For this, choose a tool for writing programs in the programming language you are writing in.Take a more advanced course in your chosen programming language. Make sure you understand the following concepts well and that you can apply them with relative ease before going forward:Inputting and outputting information to users of a program.The logical flow and the execution flow of programs in procedural languages.Declaring, assigning and comparing variables.Branching programming constructs such as if..then..else and select/switch..case.Looping constructs such as while..do, do..while/until, for..next.Your programming language syntax for creating and calling procedures and functions.Data types and manipulating them.User defined data types (records/structs/units) and their use.If your language supports overloading functions, understand it.The memory accessing methods of your language of choice (pointers, peeking, etc.)If your language supports operators overloading, understand it.If your language supports delegates/function pointers, understand itApply the advanced techniques you've learned.Object-oriented paradigm.Take an introductory course in at least one more programming language in another programming paradigm. It is recommended to learn one programming language of each paradigm, and most advanced programmers do, however, you usually start with one, work for a while applying your knowledge and practicing it, then learn the other later on, after you already had a real-life experience in programming. Try one of the following language areas:Logic programming paradigm.Functional programming paradigm.Try to compare the two programming languages you learned so far. Evaluate the strengths and weaknesses of each one. Usually this is done by:Taking simple samples of your early work in the first programming language and re-write it using the second programming language.Creating a new project and try implementing it using both languages. Sometimes, depending on your choice of project and languages, you might not be able to implement the project in one of the languages!Writing a cheat-sheet or summary-table comparisons between similar constructs in the two languages and features unique to each of the languages.Try finding ways to mimic features that is unique to one of the two languages using the other language.Learn visual programming concepts using one of the languages you learned. Almost all programming languages have versions/libraries that support visual programming and others supporting console or console-like programming. This can be accomplished by:Get an introduction to event-driven programming. Most visual programming relies in some level on events and events handling (using the programming language you choose).Try as much desktop software as you can and understand what the software does. Most software development companies offer beta-testing versions of their products which you can use to test the software. Keep up-to-date on user interface advancements.Read some articles or tutorials on graphical user interfaces.Start applying your knowledge on small software projects you design. Try applying your programming expertise on problems you face in your day-to-day life. For example, write programs that rename files in mass, compares text files visually, copies the names of files in a directory to memory/text file, and things like that. Keep it simple at first.Create a virtual graduation project. Complete this to the end, applying the techniques of visual programming you learned so far.Widen your understanding of the visual framework/library/package you learned before by taking advanced courses, paying extra attention to detail and learning more tips and tricks for your framework from online resources.Search for other packages/libraries of visual elements for your programming languages and learn them.Take a course in graphics (not graphics design). It will be very helpful for programmers wanting to write appealing user-interface elements.Become a games programmer (optional). Game programming is considered, in most of its parts, desktop programming. If you intend to become a games programmer, you will need to learn more about game programming after you finish these steps. A graphics course is a must for game programmers and the second language of choice in the preceding steps should be a logic/functional programming language (preferably Prolog or Lisp).Method 3 :- Distributed application programmingTackle distributed applications programming. Distributed application programming is considered by many to be one of the hardest to learn and requires diverse knowledge in computer and communication technologies.Take a speed introduction to telephony systems and their hardware. This step is optional. However, it is very useful in understanding network topologies.Familiarize yourself with networking hardware architectures and devices such as hubs, switches and routers.Take a course in networking protocols and essentials. You need a good understanding of the Open Systems Interconnection (OSI) model, Ethernet, IP, TCP, UDP and HTTP before you start programming distributed applications.Learn the XML language and familiarize yourself with it.Start by learning a shell scripting language. For Windows-based programming, that would be any script that works with Windows Scripting Host. For Linux-based programming, Bash scripts and Perl will be sufficient. JavaScript is strongly recommended for this in both platforms for the following reasons:It is supported by almost any scripting host in any operating system (Windows Scripting Host supports JavaScript by default, most Linux distributions has a package for JavaScript scripting console support).It is considered to be easier to learn by many developers.It has an ALGOL derived syntax which familiarizes you with much more other programming languages when you need to choose a second programming language (C, C++, C#, Java and J# all have ALGOL derived syntax).By learning JavaScript, you familiarize yourself with client-side scripting of web pages which is a bonus side-effect!Apply only procedural programming using your scripting language of choice at first. Later, you can use more advanced programming techniques and paradigms according to your scripting language and what it supports. All scripting languages have some procedural programming aspects at some level.Use the scripting language you learned to write scripts that perform communications between machines. Learn what is necessary for doing that. Simple communications will suffice.Make a transfer to a desktop scripting/programming language. Preferably, one that is a multi-paradigm language such as Python. Take a simple introduction to that second language. Java is considered by most programmers to be the language of choice for many reasons. However, C# is gaining momentum fast in this field. Java and C# are preferred for the following reasons:They are object oriented programming languages which shields programmers in large teams from implementation details as they both supports components (units of code, pre-compiled, that perform a certain task and can be used in other programs).They support event-driven programming, as well as OO and procedural programming at some level.The framework that the language is built upon is distributed by nature (in the case of Java).The availability of many ready-made packages that deal with networking, both as open-source code and framework built-in packages; this makes it easier for programmers to build upon the work of others.Concentrate more on the core features of the language, especially those supporting networking. Pay less attention to user-interface elements such as outputting, window design and techniques, and user-interface elements.Take a course on distributed applications design and architectures. This can be done using books, online tutorials or academic courses. However, understanding the architecture of distributed applications and its concepts is necessary.Learn about building serviced components and services using your programming language of choice.Learn one or more of the following technologies. It is recommended that you get at least an introduction to all of them. Most distributed application programmers do not stop at one or two programming languages, but learn at least one programming language on each operating system. That is because if you want your application to be "distributed", you should provide a version of it at least for each major operating system.Common Object Request Broker Architecture (CORBA)Simple Object Access Protocol (SOAP)Asynchronous JavaScript and XML (AJAX)Distributed Component Object Model (DCOM).NET RemotingXML Web ServicesMethod 4 :- Library/Platform/Framework/Core ProgrammingKnow what core programming is. Core programmers are merely advanced programmers who made the transfer from programming applications to programming code units to be used by other programmers.Learn a programming language that supports building reusable components/packages, if you have not done so already.Take an advanced course in UML and ORM. Most library developers use one or both of them.Take a course in software engineering.Learn at least modular, component-based, object-oriented, and event-driven programming techniques and concepts. The more programming paradigms and languages you cover, the more successful you become as a library/package programmer.Learn more about the different operating systems and programming frameworks supported by these operating systems.Focus your learning efforts on platform-independent frameworks, programming languages and technologies.If the programming languages you learned so far have ANSI/ISO/IEEE/W3Cstandard versions, master the standards. Try to use standard code whenever possible.Try to mimic simple, already established libraries, especially open-source ones.This is useful during the early phase of becoming a library/package programmer. Start with simple packages like units conversion and intermediate scientific calculations packages. If you are a college student, make use of your non-programming courses by trying to implement their equations and scientific core as libraries.Search for and try open-source packages in your field of programming. First download binaries/executables of the package. Try to use it and find its strong and weak points. After you've done that, download the source and try to figure out how it was done. Try to recreate those libraries or parts of them. At first, do that after you've seen the code and later before you see the code. At later phases, try improving those libraries.Learn the different approaches used to distribute and deploy components to programmers.Usually, library/package programmers tend to think recursively and/or iteratively of all problems they are presented with. Try to think of each problem as a collection of smaller problems (a sequence of simpler tasks) or as a repeated process of reducing the problem's scope to smaller scopes and then piling those scopes upon each other.Library/package programmers tend to generalize. That is, when presented with a simple specific problem, they usually think of a more general problem and try to solve that general problem which will automatically solve the smaller one.Method 5 :- System ProgrammingUnderstand what system programming entails. System Programmers deal with the science of programming not the specific implementations of it. Do not tie yourself to a specific platform.Follow the first three steps for Desktop Applications Programmers.Take an introductory course in Linear Algebra.Take a course in Calculus.Take a course in Logic and/or Discrete Mathematics.Introduce yourself to different bare operating systems. This can be done by:Getting an idea on how operating systems are installed.Learning how to install different operating systems on one PC (optional, but recommended).Installing more than one operating system. Do not install any helping packages on the systems; instead, use the bare functionalities provided by the operating systems.Take a course (or alternatively, read books) on computer hardware architecture.Develop an understanding of the different computer hardware platforms.Get an introductory familiarization with the assembly language of the hardware platform/operating system of choice. You will later learn the assembly of other platforms/systems.Learn the ANSI C and C++ languages, along with the concepts of procedural programming.Understand and practice C/C++ standard libraries on the platform of choice.Pay particular attention to Standard Template Library (STL) and maybe Active Template Library (ATL).Search online resources, books and courses to get an understanding of the C-flavor of your specific platform.Practice creating advanced code with C and C++.Learn more advanced Assembly.Take a course in operating systems design.Find and read documentations of your specific platform of choice. This will be easier if you choose a Unix-based operating system. Understand the system you will be working with later very well.Practice your acquired knowledge. First create small system utilities. It is usually useful to:Trying to recreate small tools that are already there on your system.Trying to port utilities available in other operating systems to yours.Learn languages in the most helpful order. This is the only place where the first programming language matters. Learn ANSI C first, not C++, not C#, not Java and not D. Then learn C++.Restricting the first language to C and C alone is because systems programming requires that the programmer be familiar with the following concepts:Real and full compilation of source code.Low-level object output files.Linking binaries.Low-level machine-language/assembly programming. The C language is said to be a disguised/easier to learn assembly by some. It also supports inserting assembly language code in code whenever you please and it is only procedural (like assembly).Method 6 :- Programming ScienceKnow what a programming scientist does. Programming scientists are very advanced programmers who, instead of working on developing applications, work on developing computing technologies such as encryption, programming languages and data mining algorithms. This level is seldom achieved without academic study and dedication.Accumulate the scientific knowledge equivalent to a four-year degree in computer science. This can be done either by:Taking an actual academic degree (which is what usually happens).Getting the courses' outlines for such a degree from one of the modern universities and taking the courses either by self-study or as separate courses. This could be achieved theoretically, but the recommended path is the first.Decide a field of specialty. The more specific, the better. This depends on your preferences. However, here is a list of some of the major topics in computer programming science:Algorithm design (searching, sorting, encryption, decryption and error detection in communications are some examples)Programming languages/compiler design/optimizationArtificial intelligence fields (pattern recognition, speech recognition, natural language processing, neural networks)RoboticsScientific programmingSuper computingComputer aided design/modeling (CAD/CAM)Virtual realityComputer graphics (Computer graphics is usually wrongly confused with graphical design or graphical user interface design. Computer graphics is the field of studying how to represent and manipulate graphics in computer systems.)Consider getting a higher academic degree. You might wish to pursue a master's degree or a doctorate.Learn the technologies and programming languages related to your programming field of choice.