Monthly Progress Report And Programme Course Completion Form: Fill & Download for Free

Hy,The total fee to be paid for the Autumn Semester 2017-17 is Rs.67,876 (for GEN / OBC candidates) and Rs. 22,876 (for SC/ST/PD).If you choose for hostel accommodation at IIT Bombay, you must pay Rs. 16,000 (Rs. 2,000 refundable mess advance + Rs. 13,000 Semester mess advance + Rs.1,000 towards Bus pass for internal transport for IITB Campus.)Edit- tuition fee is 1 lakh per semester.(in image)1. All the SC/ST/PD students are exempted from payment of Tuition Fee.2. Institute has two semesters in a year i.e. Autumn & Spring.3. Bachelor of Technology (B. Tech.) is a year (8 Semesters) Programme.4. Dual Degree/ 5 yr Integrated M. Sc. is a 5 Year (10 Semester) Programme.5. IIT Bombay reserves right to revise fee structure in subsequent semester.*one time fee only.**Payable in Autumn Semester (July – December) every year..(☆~ signifies heading)☆~PG Level Programme for Working Professionals :The registration for the course(s) can be done at the CEP office. At the time of registration, the candidate should also pay the course fees of Rs. 6,000/- (Rs. Six thousand only) per course, payable by way of Demand Draft in favour of "Registrar, IIT Bombay, (CEP Account)".(The fees cannot be refunded once the registration is done).☆~MDP Course on Resource Mobilization for NGOsRs.30000/- per participant, (if a NGO sends more than one participant then fees applicable will be Rs. 25000/- per participant) covers, tuition, course material, registration, lunch, tea/coffee served during the programme days. The course is non residential and accommodation will not be provided on IIT Bombay campus to the participants. Participants will make their own arrangements of transportation to and from SJMSOM to their destinations. The fees are payable in advance by demand draft drawn in favour of .Registrar, IIT Bombay. All payments should be sent to SJMSOM through SOSVA.A prize is awarded to the participant by SOSVA, for the most successful strategy adopted by him/her in his/her organisation, post 12 months of course completion.☆~IIT Bombay Research Fellowship Scheme~The awardees are selected on the basis of their academic record followed by an interview, and a written test. They are required to work on research projects for a minimum period of two years in different Departments/ Schools/ Centres/ Inter-disciplinary Programs based on their academic background and research interests.The Research Fellowship Scheme is open to B.Tech. / B.E. / M.Tech. / MBA / M.A. / http://M.Com. / http://M.Sc. / MBBS or equivalent degree, with or without experience. Students with research inclinations in Humanities, Bio-sectors, Management and other background are also eligible.The awardees with B. Tech/B.E. or http://M.Sc qualification will are awarded a stipend of Rs. 8,000 - 10,000/- p.m. Degree holders with experience can expect higher amount.Ancillary benefits include funding for participation in national & international conferences, Library facilities and hostel accommodation.IIT Bombay provides assistance to iuts students in three forms- Scholarship, Research Fellowship and Research Assistants.☆~SCHOLARSHIPS~~IIT Bombay provides assistance to its students in three forms- Scholarship, Research Fellowship and Research Assistants.The students of IIT Bombay receive scholarship not just from the institute but also from the private organizations. Government also recognises the brilliant students of IIT Bombay and awards them through the scholarship route.☆~IIT Bombay Scholarship 2017For all students:1. Merit-cum-Means (MCM) Scholarship: Tuition fee waiver (Rs.45,000 per semester) Rs.1000 per month for both semesters in the academic year2. Free Tuition: Tuition fee waiver (Rs.45,000 per semester)For SC/ ST students:1. Free Tuition: Tuition fee waiver (Rs.45,000 per semester)2. Free Messing: only for basic menu Pocket allowance of Rs. 250 per month for both semesters in the academic year; Exemption from payment of hostel room rentFor PD students:1. Free Tuition: Tuition fee waiver (Rs.45,000 per semester)Private scholarships1. Named Merit Scholarships: Rs. 25,000 - Rs.1,50,000 per year2. Named Merit-cum-Means Scholarships: Rs. 25,000 - Rs.1,00,000 per year.☆~Private ScholarshipStudents at IIT Bombay are awarded scholarships by various private organizations too. The criteria adopted by these organizations are same as followed by the IIT Bombay- 'Merit and Means'. The organizations have a separate Scholarship Trust wherein only those students can apply whose parental gross income from all sources does not exceeds beyond Rs 4, 50,000.The IIT alumni in USA have also established a fund for providing scholarships to the brilliant and deserving students of IIT under the name- IIT Bombay Heritage fund Scholarships. The alumni collect funds from all the members of the alumni for the constitution of the scholarship to be awarded to large number of students. The selection criteria for availing scholarship under the IIT Bombay Heritage fund are same as the criteria set up by the Private Organisations.A student can get the full details from the Academic office of the Institute.☆~Government ScholarshipThe students of IIT Bombay can apply for scholarship to the Government besides applying for the same in their institute. The Central government awards National Scholarships to students of every state on the basis of marks scored by them in the previous academic year. A student interested in renewing his/her scholarship also has to submit the progress report of the last academic year.However, those students who have already received the scholarships by appearing in the National Talent Search Examination of the NCERT need not apply to the government again. The NCERT scholarship can be availed till one finishes his/her Masters' programme.☆~Research Fellowship~IIT Bombay awards 100 Research fellowships each year to the students belonging to different colleges across the country, for undertaking research project at its different departments. The Research work is allotted on the basis of a student's academic qualification.The students short listed for the fellowship scheme need not necessarily belong to an engineering background. The fellowship is open even for post graduates in other streams too. For undertaking the research work, the institute provides a fixed amount in the form of stipend. The amount, however, differs with the experience. For instance, the students who are in III year of their B.Tech programme or are in II year of M.A. MSC., http://M.Com and MBA receive Rs 10,000 per month as stipend. The M.Tech degree holders with some experience, on the other hands, are awarded a stipend Rs 2000 more. The students can continue their studies along with the research work.Besides the stipend, the students who are conferred Research fellowship are given a long term membership to the Institute's library for reference purposes.The applications for the fellowship programme are released in the end of December month every year.☆~Research Assistanceship (RA)~The students with a good GATE score are considered for the position of' Research Assistants' by various departments of IIT Bombay. As a Research Assistant the students has look after the concerned department institute and assist in teaching and other works. A Research assistant has to devote a total of 20 hours in a week. They are also given the liberty to complete their M.Tech programme in three years instead of the☆~SUMMER INTERNSHIP SCHEME-The awardees (who have not completed their degrees) have an opportunity to work on projects under the supervision of faculty members/scientists during their summer vacation. On the completion of their degrees, they could be considered for research Fellowship subject to performance during Summer Internship. The Summer Internship scheme is open to pre-final year B.Tech./ B.E./ M.A./ http://M.Com/ http://M.Sc./ M.E./ M.Tech./ MBA/ MBBS or equivalent degree students.Rs. 3000/-p.m. for summer internship (For a maximum of two months).Other benefits include Library facilities and hostel accommodation.SELECTION PROCEDURE(Selection Procedure for Summer Award of Scholarships)The selection procedure consists of preliminary screening of applications, followed by personal interviews that take place in a decentralized manner at the department level.Selection criteria includes consistently brilliant academic performance (relative rank in the class/University), work carried out by the student besides his regular academic work, participation/recognition in competitions such as Techfest, Olympiads, National Talent Search Exams, and performance in the interview.Happy to help you.HOPE IT WILL HELP YOU.THANK YOU.

One day after I wrote the following review, Quora made a rule against reviews of people. So I've pasted the text and images from my review me below.Marcus's rating: 2 1/2 stars.Marcus is a reasonably smart guy who often comes across as smarter than he is, because he's a good communicator who happens to have a wide range of interests, some of which are "heady," such as Shakespeare and Philosophy. He's not a terribly original thinker, but he enjoys thinking and discussing. The most frustrating thing about Marcus offline is his anxiety, which can be taxing to others as much as to him.Online, that's not as evident. In Internet discussions, he can be excessively fair-minded. That sounds good, and it often is, but it has its downsides. Marcus has an almost pathological hatred of teams. He generally refuses to take sides, so if he's agreed with you in the past, you can't count on him to agree with you in the future. You also can't count on him to support a cause without also criticizing it and without also continually bringing up the valid points of the other side.All this is well and good if the point of the discussion is diplomacy or learning. But if strong, political action is what's needed, it's often necessary to simplify things a little -- to be loyal to a cause or side, ignoring the ways that side is wrong and not focusing on the ways the other side is right. Marcus is pretty useless (or even counter-productive) in those circumstances.He is a high-functioning Aspie, which means he often interprets things literally. Though this is his nature, he has learned to overcome it and is capable of thinking in a looser, more-interpretive way. But he often chooses not to. As with his even-handedness, this is a double-edged sword. It's great to have him around when you want to get to the bottom of some issue and examine its underlying assumptions. His literalism tends to further that goal, because he hates vagueness and urges people to clearly define their terms.But if you're looking for a more "brainstorming-like" discussion, in which ideas are used bounced around and no one dwells for too long on nitty-gritty details, Marcus can be a royal pain in the ass. He sucks at "just shootin' the shit." And if you originally want an in-depth discussion but then decide you've had enough, Marcus may refuse to let go.It's nearly impossible to get him to fight with you. Again, this sounds like a virtue, and in many ways it is. But fighting is a part of human discourse, and some people work out ideas and emotions through (antagonistic) arguments. Marcus won't bite. He often responds to sarcasm as if he can't detect it. Tell him "Oh yeah, Marcus, that's a really terrific idea..." and he'll probably say, "What do you like about it?" Or, if he detects the sarcasm, he may say, "It sounds like you're being sarcastic, so my guess is you don't think it's a good idea. What don't you like about it." (Marcus's mom is a psychotherapist. His dad is a professor. Go figure!) This is extremely annoying if what you want is to vent or if you just want to exchange a couple of barbs and be done with it.Since this review is being written by Marcus, all of the above sounds passive-aggressively self-serving. And in fact, Marcus can be passive aggressive. He's a huge fan of Quora's "Be Nice Be Respectful Policy," and, in fact, has a much more involved (but similar) policy for himself: Marcus's Rules Of Order For Himself (Which He Invites You To Follow). His non-combative approach is fantastic when it's possible. But he's so wedded to it, he risks stifling his own aggressive urges.And when he does that, his aggression won't just go away. In the worst cases, it pops out in some underhanded, passive-agressive form which is hard to argue with, because it sounds reasonable. In the best of all possible worlds, people always treat each other with respect. But when that's impossible, open and honest aggression is better than subtextual aggression. Marcus doesn't always understand that.He also can forget that sometimes people just need to fight. As a child, he would often play diplomat, explaining his dad's point-of-view to his mom and his mom's point-of-view to his dad. More than once, he had to be told, "Sometimes you just have to let mommy and daddy fight!" or, more bluntly, "Stay out of it. It's none of your business."Sometimes, Marcus comes across as humorless, which is too bad, because he's extremely playful, silly and even bawdy. But he has trouble integrating that part of himself into his more intellectual posts. People who first "met" him on sites like Quora and then got to know him in person or on Facebook are often surprised by his real-life childishness, prankishness, and irreverence. On Quora, he sometimes indulges this side of himself, here: The Glove Is Losing Its Touch.Marcus is good at a few things but not great at anything. He's a good writer, but he's not Hemingway; he's a good theatre director, but he's no Peter Brook; He's a good programmer but he's not Linus Torvalds; he dabbles in History, Psychology, Art and some other fields without being an expert in any. He can't call himself a "polymath" with a straight face. He's a dilettante.And he lives way too much in his head. The closest thing he as to nuts-and-bolds skills is programming, but even that's a far cry from carpentry, sculpting, electronics, football playing, knitting, etc. He comes across as someone scared to get his hands dirty, and he's ashamed of the fact that he mostly lives above the neck. He's much more impressed by people with real-world skills than he is by navel-gazers, which is basically what he is. He could, of course, learn to play the guitar or fix broken toilets, but he's too lazy and scared to apply himself this way.Like most Aspies (and some neurotypical people) Marcus has obsessions. The benefit of which is deep immersion, allowing Marcus to see small details that other people tend to miss. It's not that he's more eagle-eyed. He's just relentless. If you spend hours, months, and years examining a subject, you're bound to spot hidden details after a while.The problem with obsessions is that they can be tedious to others. There are topics which, frankly, Marcus just won't shut the fuck up about, even after everyone has grown weary. For instance education: Wounded by School. And his anti-team stuff can also get old fast. Marcus's obsessions can make him tedious and long-winded (as with this review). You may feel an urge to brand "tl;dr" on his forehead.Marcus is so eloquent on the web and so shy offline, people from various sites sometimes push to meet with him in real life and then are disappointed and uncomfortable when his coffee-shop conversation is forced or non-existent. As with many introverts, he's gregarious with super-close friends, stymied at parties, and disabled with strangers.And, though this isn't apparent on Quora, he's a bundle of ticks and neurosis. He tend to walk around all day with anxious knots in his stomach, unable to stop himself from worrying about things he can't control. He's an insomniac, plagued with constant racing thoughts. His wife calls him "the least Zen person she's ever met." Anxiety tends to spread, and so Marcus can sometimes make others nervous.His best qualities are gentleness, patience, an extremely open mind, playfulness (if you get to know him), fairness, lateral thinking, communication skills, and attention to detail. He is generally good at learning from failure: Personal Failures. He's a good and devoted husband. His worst qualities (not already covered) are sloppiness as a housekeeper (despite his attention to detail), uselessness in a crisis (he becomes a deer in the headlights), an almost complete intolerance to physical pain, and prissiness.He's not particularly good looking. He's 5'8" and nerdy. He has terrible posture and is generally uncomfortable in his body. He never had a girlfriend in high school and for most of college. Physically, the best you can say about him is that he's not offensive looking and has good genes in terms of aging. He's 47 but people often think he's in his early 40s or even 30s. He's never suffered from any major health problems, but he's not exactly in shape, either. He strikes straight women and gay men more as someone who might be nice to cuddle with than to fuck.Here are some surprising facts about Marcus:- He collects oddball antiques and weird toys. His wife, Lisa, loves anything morbid and, over the years, Marcus has grown to share that passion: he has a miniature guillotine, a full-size model skeleton, a tiny bear made out of belly-button lint (found on etsy), many robot and monster toys and sculptures, a taxidermied cobra, a mummified frog (which he mummified himself), a dead octopus in a jar, many puppets and stuffed animals, a few hand-crafted ray guns, an antique cash register, two huge wooden oars, and so on.- He can't read comic books. For some reason, his brain has trouble parsing non-linear text. Whenever he picks up a graphic novel, he gets confused and weary having to scan all over the page. This is a new development. As a kid, he loved comics and collected them. He was never into superheroes. His favorites were "Howard the Duck," "Man Thing," and "Swamp Thing."- He nearly flunked out of high school and cheated his way into college. It took him a long time to be successful in academia. He kept dropping out and took ten years to complete his undergraduate degree. He eventually (and grudgingly) committed to getting good grades and even completed an MFA degree in Directing. But the only good memory he has of school is meeting his wife.- He has stupid tastes in food, preferring childish comfort food to gourmet cuisine. An ex-girlfriend said he never developed an adult palette. His favorite foods include pop-tarts, circus peanuts (the candy almost everyone in the world is disgusted by), spaghettios, and PB&J sandwiches. He is a Supertaster and an annoyingly picky eater. He hates mustard, ketchup, mayonaise, vinegar, pickles, tomatoes and mushrooms. And by "hates," I mean he'll start gagging if he even smells mustard or tastes a tomatoes seed. He prefers crappy office coffee (e.g. Sanka) to good stuff from a coffee shop.- He's 100% straight. He's never had even a momentary fantasy about doing anything sexual with a man, and he doesn't even have an aesthetic sense about men. When people say guy A is better looking than guy B, Marcus is often puzzled. Despite this, all of his best male friends have been gay. He generally gets along with gay men and women better than with other straight men. He doesn't really know how to do the "guy thing." He's not a "bro."- He's a leg man. He's so distracted by female legs, that in the summer, when shorts and short skirts are in, it often seems to him like women in NYC are walking around naked. He's a respectful, middle-aged guy, and he doesn't gawk or make women feel uncomfortable. But it's sometimes hard for him to concentrate when there are too many legs in the vicinity. Strangely, he's not into feet. He barely notices that people exist below the ankles.- He's also a big fan of faces -- especially noses. He thinks his wife has the best nose he's ever seen, though she's puzzled by what he likes about it, and he can't explain it. It's just an awesome nose! As for other sexual stuff, you'd never think it to look at him, but he's been through some extremely "experimental" phases. But you have to get to know him really well for him to open up about them.- He doesn't like "Star Wars," "Lord of the Rings," or "Star Trek," but he's a huge fan of "Lost in Space," the old TV series.- He first learned to program computers using punch cards.- He's a polite, learned guy, who reads Shakespeare, listens to classical music, and loves old movies. But his favorite words are fuck, cunt, and shit. He loves swearing.- He's extremely ticklish and is seriously considering deleting this bullet point. He hates being tickled and has trouble breathing if someone even threatens to tickle him.- His temporary memory is almost non-functional. He can't remember a phone number -- even for ten seconds. By the time he hears the last four digits, he's forgotten the first three.- He once shoplifted a small monster "stuffed animal." When he saw it, he was overwhelmed with a feeling that it was his and so he shouldn't have to pay for it. He's not a thief in general, so it's an odd exception. That was twenty years ago, and he still has the little monster. He feels no guilt about stealing it.- He doesn't use drugs and is close to being a teetotaler. He's not prudish or judgmental about it. and he's tried pot and has the occasional drink with dinner (likes the taste of wine; hates beer). He's simply never enjoyed the experience of being drunk or high. He's scared of harder drugs, and both pot and alcohol tend to affect him physically but not mentally. That is, he remains totally lucid but starts losing control of his limbs.- He likes to talk about poo. He mostly confines these discussions to being between him and his wife. They loudly announce they're about to poo, give progress reports from inside the bathroom, and debrief each other after the event. For Marcus, this is partly a way of challenging his natural prissiness. He's a germaphobe who hates bathrooms -- especially public ones. He plays against it. And he truly believes that part of the secret to his successful marriage is the fact that he and his wife talk about poo together.

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.