The do’s and don’ts of CV writing

cv imageThe way you present your CV can have an overwhelming influence over whether your CV is even read, let alone get you that all important interview. CV’s are different in name only, they describe the same thing: A document that chronicles personal details, career history and achievements!

You will need to consider what to include, how much detail is needed and how to make your CV stand out from all the others. Here are the golden rules to abide by when constructing your CV:


  • Construct your CV with your prospective employer in mind. Make it easy for them to conclude that you are a strong candidate.
  • Tailor your CV to the job. Your CV should be tailored for the job you’re applying for, focusing on the parts that are important for that particular job.
  • Use a clear, uncluttered layout, without too many special effects. If you want to use bold text, indentation or even frames, feel free—but keep in mind that the ultimate goal is to make the CV a quick read.
  • Use positive language. when describing your work achievements use power words such as ‘launched’, ‘managed’, ‘coordinated’, ‘motivated’, ‘supervised’, “liaised” and ‘achieved’.
  • Explain all significant breaks in your career or education
  • Place the important information up-front. Put experience and education achievements in reverse chronological order starting with your current job and working backwards.
  • Give a brief describe of the companies that you worked for and your major achievements in bullet form.
  • Include experience and interests that might be of use to the employer: IT skills, voluntary work, foreign language competency, driving skills, leisure interests that demonstrate team skills and organization/leadership skills.
  • Put your name and email address on every page.


  • Include salary information and expectations. Leave this for negotiations after your interview.
  • Include information which may be viewed negatively – failed exams, failed business ventures, reasons for leaving a job, points on your driving license. Don’t lie, but just don’t include this kind of information. Don’t give the interviewer any reason to discard you at this stage.
  • Be tempted to shrink the font or reduce the margins to get more information in. Keep it easy to read. If you need to say more, use another page, but ask yourself if the extra detail really adds value.
  • Include referees – just state they are available on request.
  • Include all of the jobs you have had since school, just the relevant ones. Add details about your most recent qualifications, which are more relevant, but summarize the rest.
  • Use jargon, acronyms, technical terms – unless essential.
  • Include a photo unless requested.



Eleanor Collins

If You Want To Learn Data Science, Take These Statistics Classes

A year ago, I was a numbers geek with no coding background. After trying an online programming course, I was so inspired that I enrolled in one of the best computer science programs in Canada.
Two weeks later, I realized that I could learn everything I needed through edX, Coursera, and Udacity instead. So I dropped out.
The decision was not difficult. I could learn the content I wanted to faster, more efficiently, and for a fraction of the cost.
I already had a university degree and, perhaps more importantly, I already had the university experience. Paying $30K+ to go back to school seemed irresponsible.
I started creating my own data science master’s degree using online courses shortly afterwards, after realizing it was a better fit for me than computer science. I scoured the introduction to programming landscape. For the first article in this series, I recommended a few coding classes for the beginner data scientist.
Now onto statistics and probability.
I have taken a few courses, and audited portions of many. I know the options out there, and what skills are needed for learners preparing for a data analyst or data scientist role.
For this guide, I spent 15+ hours trying to identify every online intro to statistics and probability course offered as of November 2016, extracting key bits of information from their syllabi and reviews, and compiling their ratings. For this task, I turned to none other than the open source Class Central community and its database of thousands of course ratings and reviews.
Since 2011, Class Central founder Dhawal Shah has kept a closer eye on online courses than arguably anyone else in the world. Dhawal personally helped me assemble this list of resources.
How we picked courses to consider
Each course must fit four criteria:
  1. It must be an introductory course with little to no statistics or probability experience required.
  2. It must be on-demand or offered every few months.
  3. It must be of decent length: at least ten hours in total for estimated completion.
  4. It must be an interactive online course, so no books or read-only tutorials. Though these are viable ways to learn statistics and probability, this guide focuses on courses.
We believe we covered every notable course that fits the above criteria. Since there are seemingly hundreds of courses on Udemy, we chose to consider the most-reviewed and highest-rated ones only. There’s always a chance that we missed something, though. So please let us know in the comments section if we left a good course out.
How we evaluated courses
We compiled average rating and number of reviews from Class Central and other review sites. We calculated a weighted average rating for each course. If a series had multiple courses (like the University of Texas at Austin’s two-part “Foundations of Data Analysis” series), we calculated the weighted average rating across all courses. We read text reviews and used this feedback to supplement the numerical ratings.
We made subjective syllabus judgment calls based on three factors:
  1. The degree to which each course teaches statistics through coding up examples – preferably in R or Python.
  2. Coverage of the fundamentals of probability and statistics. Covering descriptive statistics, inferential statistics, and probability theory is ideal.
  3. How much of the syllabus is relevant to data science? Does the syllabus have specialized content like genomics, as several biostatistics courses do? Does the syllabus cover advanced concepts not often used in data science?
Why Target Coding?
William Chen, a data scientist at Quora who has a master’s in Applied Mathematics from Harvard, wrote the following in this popular Quora answer to the question: “How do I learn statistics for data science?”
For any aspiring data scientist, I would highly recommend learning statistics with a heavy focus on coding up examples, preferably in Python or R.
Since a lot of a data scientist’s statistical work is carried out with code, getting familiar with the most popular tools is beneficial.
Statistics AND Probability
Probability is not statistics and vice versa. My favorite explanation of their differences is from Stony Brook University:
Probability deals with predicting the likelihood of future events, while statistics involves the analysis of the frequency of past events.
They explain that “probability is primarily a theoretical branch of mathematics, which studies the consequences of mathematical definitions,” while “statistics is primarily an applied branch of mathematics, which tries to make sense of observations in the real world.”
Statistics is generally regarded as one of the pillars of data science. Probability – though it generates less attention is also an important part of a data science curriculum.
Joe Blitzstein, a Professor in the Harvard Statistics Department, stated in this popular Quora answerthat aspiring data scientists should have a good foundation in probability theory as well.
Justin Rising, a data scientist with a Ph.D. in statistics from Wharton, clarified that this “good foundation” means being comfortable with undergraduate level probability.
Our picks for the best statistics and probability courses for data scientists are:
“Foundations of Data Analysis” includes two of the top reviewed statistics courses available with a weighted average rating of 4.48 out of 5 stars over 20 reviews. The series is one of the only courses in the upper echelon of ratings to teach statistics with a focus on coding up examples. Though not mentioned in either course titles, the syllabi contain sufficient probability content to satisfy our testing criteria. These courses together have a great mix of fundamentals coverage and scope for the beginner data scientist.
Michael J. Mahometa, Lecturer and Senior Statistical Consultant at the University of Texas at Austin, is the “Foundations of Data Analysis” series instructor. Both courses in the series are free. The estimated timeline is 6 weeks at 3-6 hours per week for each course. One prominent reviewer said:
Excellent course! I took part 1 and enjoyed it a lot, so it was very easy to decide to go on with part 2. Dr. Mahometa and team are very good teachers and their material is of a very high quality. The exercises are interesting and the materials (videos, labs and problems) are appropriate and well chosen. I recommend this course to anyone interested in statistical analysis (as an introduction to machine learning, big data, data science, etc.). On a scale from 1 to 10, I give 50!
Please note each course’s description and syllabus are accessible via the links provided above.
A stellar specialization
Update (December 5, 2016): Our original second recommendation, UC Berkeley’s “Stat2x: Introduction to Statistics” series, closed their enrollment a few weeks after the release of this article. We promoted our top recommendation in “The Competition” section accordingly.
�¢?�¦which contains the following five courses:
This five-course specialization is based on Duke’s excellent Data Analysis and Statistical Inference course, which had a 4.82-star weighted average rating over 55 reviews. The specialization is taught by the same professor, plus a few additional faculty members. The early reviews on the new individual courses, which have a 3.6-star weighted average rating over 5 reviews, should be taken with a grain of salt due to the small sample size. The syllabi are comprehensive and has full sections dedicated to probability.
Dr. Mine Ã???etinkaya-Rundel is the main instructor for the specialization. The individual courses can be audited for free, though you don’t have access to grading. Reviews suggest that the specialization is “well worth the money.” Each course has an estimated timeline of 4-5 weeks at 5-7 hours per week. One prominent reviewer said the following about the original course that the specialization was based upon:
One of the greatest courses I’ve taken so far. [Dr. Mine Cetinkaya-Rundel is] a great teacher, very much involved in exchanges with her students. A large variety of teaching approaches and tools. Lots of practice through short tests, R-programming labs, and an in-depth project. A very lively forum with lots of help to cope with difficulties. The course is not too difficult, but the variety of the proposed material requires that students get involved quite substantially. A very nice book available for free with plenty of practice exercises.
Want more probability?
Consider the above MIT course if you want a deeper dive into the world of probability. It is a masterpiece with a weighted average rating of 4.91 out of 5 stars over 34 reviews. Be warned: it is a challenge and much longer than most MOOCs. The level at which the course covers probability is also not necessary for the data science beginner.
John Tsitsiklis and Patrick Jaillet, both of whom are professors in the Department of Electrical Engineering and Computer Science at MIT, teach the course. The contents of this course are essentially the same as those of the corresponding MIT class (Probabilistic Systems Analysis and Applied Probability)Ã?¢??-Ã?¢??a course that has been offered and continuously refined over more than 50 years. The estimated timeline is 16 weeks at 12 hours per week. One prominent reviewer said:
Many online courses are watered down in some way, but this one feels like a proper rigorous exercise-driven course similar to what you’d get in-person at a top school like MIT. The professors present concepts in lectures that have obviously been honed to a laser focus through years of pedagogical experienceÃ?¢??-Ã?¢??there is not a single wasted second in the presentations and they go exactly at the right pace and detail for you to understand the concepts. The exercises will make you work for your knowledge and are critical for really internalizing the concepts. This is the best online course I have taken in any subject.
I encourage you to visit Class Central’s page for this course to read the rest of the reviews.
The competition
Our #1 pick had a weighted average rating of 4.48 out of 5 stars over 20 reviews. Let’s look at the other alternatives.
  • MedStats: Statistics in Medicine (Stanford University/Stanford OpenEdx): Great syllabus where the examples have a medical focus. Covers a bit of R programming at the end, though not as much as UT Austin’s series. A worthy option for anyone, even those not targeting medicine. It has a 4.58-star weighted average rating over 32 reviews.
  • SOC120x: I “Heart” Stats: Learning to Love Statistics (University of Notre Dame/edX): Targets a non-technical audience, though likely would be good for anyone. No coding. Good production value. Course and instructors look really fun. It has a 4.54-star weighted average rating over 12 reviews.
  • QM101x: Statistics for Business (Indian Institute of Management Bangalore/edX): Part of a 4-course series. Business focus. Good syllabus that uses coding. The last two courses in the series are unreleased as of November 2016 so can’t make a judgment yet. It has a 4.43-star weighted average rating over 27 reviews.
  • Workshop in Probability and Statistics (Udemy): Taught by Dr. George Ingersoll, Associate Dean of Executive MBA Programs at the UCLA Anderson School of Management. Costs money. Uses Excel. It has a 4.4-star weighted average rating over 452 reviews.
  • Intro to Descriptive Statistics (San Jose State University/Udacity): Part of a 2-course series. Bite-sized videos. No coding. It has a 3.88-star weighted average rating over 8 reviews.
  • Intro to Inferential Statistics (San Jose State University/Udacity): Part of a 2-course series. I took both courses as refreshers for my undergrad statistics classes and came away with a deeper understanding. Really enjoyed Katie Kormanik’s teaching style (see video below). Bite-sized videos. No coding. It has a 4.4-star weighted average rating over 5 reviews.
  • 6.008.1x: Computational Probability and Inference (Massachusetts Institute of Technology/edX): One of two courses/series to teach statistics with a focus of coding up examples in Python. Reviews suggest prior stats experience is needed and that the course is a bit unorganized. It has a 4-star weighted average rating over 12 reviews.
  • Basic Statistics (University of Amsterdam/Coursera): One of two statistics courses in the University of Amsterdam’s Methods and Statistics in Social Sciences Specialization. One exceedingly positive review on the series and its instructors. No coding. It has a 4.06-star weighted average rating over 8 reviews.
  • Inferential Statistics (University of Amsterdam/Coursera): One of two statistics courses in the University of Amsterdam’s Methods and Statistics in Social Sciences Specialization. One exceedingly positive review on the series and its instructors. No coding. It has a 4-star weighted average rating over 3 reviews.
  • PH525.1x: Statistics and R (Harvard University/edX): Part of a 7-course series on edX. Life sciences focus. Uses R programming, but the reviews suggest UT Austin’s series is better. It has a 3.96-star weighted average rating over 26 reviews.
  • Intro to Statistics (Udacity): This is one of Udacity’s earliest courses and it has its shortcomings, as described in this memorable review by a college educator. No coding. It has a 3.93-star weighted average rating over 41 reviews.
  • KIexploRx: Explore Statistics with R (Karolinska Institutet/edX): More of a data exploration course than a statistics course. Uses coding. It has a 3.77-star weighted average rating over 22 reviews.
  • Statistical Inference (Johns Hopkins University/Coursera): One of two statistics courses in JHU’s data science specialization. Bad reviews. It has a 2.9-star weighted average rating over 29 reviews.
  • Regression Models (Johns Hopkins University/Coursera): One of two statistics courses in JHU’s data science specialization. Bad reviews. It has a 2.73-star weighted average rating over 30 reviews.
  • DS101X: Statistical Thinking for Data Science and Analytics(Columbia University/edX): Part of the Microsoft Professional Program Certificate in Data Science. Short syllabus. Bad reviews. It has a 2.77-star weighted average rating over 24 reviews.
  • Understanding Clinical Research: Behind the Statistics (University of Cape Town/Coursera): “This isn’t a comprehensive statistics course, but it offers a practical orientation to the field of medical research and commonly used statistical analysis.” Health care focus. It has a 5-star weighted average rating over 15 reviews.
  • MED101x: Introduction to Applied Biostatistics: Statistics for Medical Research (Osaka University/edX): Biostatistics focus. Uses coding. It has a 4.5-star weighted average rating over 3 reviews.
  • Probability and Statistics (Stanford University/Stanford OpenEdx): Curriculum looks great. The one review is really positive. No coding. It has a 4.5-star weighted average rating over 1 review.
The following courses had no reviews as of November 2016.
Wrapping it Up
This is the second of a six-piece series that covers the best MOOCs for launching yourself into the data science field. We covered programming in the first article, and the remainder of the series will cover several other data science core competencies: the data science process, data visualization, and machine learning.
The final piece will be a summary of those courses, and the best MOOCs for other key topics such as data wrangling, databases, and even software engineering.
Courtesy: David Venturi, FreeCodeCamp

Women of Substance – BISP showcased case studies

bis2.jpgBenazir Income Support Programme (BISP) held an event on Tuesday morning at its auditorium to showcase and screen the inspiring case studies of BISP Beneficiaries. The event was attended by an IMF representative, the Country Director, World Bank Pakistan and the economic growth unit head, DFID Pakistan. Also, a number of development practitioners attended the event.  The anecdotes of 12 of these inspiring women of substance are as follows:

 “We were poor and used to break stones. Stipend provided by the BISP empowered me to support my family and now I use this amount for the education of my son and the medical needs of the family.” Gulfam Bibi  

“After the death of my husband, I was left alone with my small children to make my ends meet. The stipend given by BISP helped me raise my children and support their education in a respectable manner.” Taj Bibi from Havelian KP.

“My husband is jobless and struggles with his health issues. The BISP stipend helped me to dig for clean water and install washroom facility at my home. The continued support from BISP is helping me in educating my children”. Shabana Parveen, Dadu Sindh

“Due to blood pressure and high sugar issue, my husband couldn’t go out and wok. This encountered us with financial burden. BISP helped me and I bought a sewing machine and learned the art. Now I am earning to help my family.” Asifa Safdar, Quetta Baluchistan

“After earthquake in 2005, my family suffered a lot and husband couldn’t continue his small work of petty business. Later when BISP started helping me through the stipend, we felt better. Now we are spending this stipend on education and health.” Masooda Begum, Dhirkot AJK.

“I have ten children and they were not going to school because my husband was physically challenged. When BISP provided me stipend, it helped me in enrolling my children to school and meet the basic necessities at home.” Pervez Begum Gilgit, GB

“We were trash-pickers but when BISP made us its beneficiaries, we left trash picking and started making moorah (stools). Now we are earning too and it is much better than trash-picking.” Munawar Khatoon, Chakwal, Punjab

 “The floods in KP destroyed our house and we became homeless and took refuge in tents. BISP approached us and started providing stipend. Through this I learned sewing and flowering. Now I am earning this way.” Irshad Bibi Nowhsera, KP

 “My husband encountered an accident and couldn’t continue working. This made our life miserable. Unwillingly, I let me son to go for work and feed us. Luckily, BISP considered us for its stipend program. Now my children are getting education and we are feeling better.”  Naseem Akhtar, Palandari AJK

“Unfortunately my husband became ill and lost his eye-sight. This compelled me to go out for work to meet our ends. BISP stipend enabled us to meet the educational expenses of our children and spend on health needs. Now I feel privileged and empowered.” Gasmali Ghizar, GB

“My husband works on daily wages and he doesn’t have any proper source of income. Through BISP’s stipend we send our children to school and spend a comparatively better life.” Rehana Begum, Jhal Magsi, Balochistan.  

“I got married at the age of 12. Since then my husband and I struggled to meet our ends but we didn’t succeed in educating our children or completing our basic needs but the stipend of BISP came like a ray of hope to alter our livelihood.”  Sri Nandani, Jacobbabad Sindh.


Paul Romer and William Nordhaus won 2018 Nobel Prize in Economics

economic nbl

2018 Nobel Prize in Economics was jointly awarded to Paul Romer of New York University and William Nordhaus of Yale for their work on economic growth and climate change innovation in economic analysis.

“Their findings have significantly broadened the scope of economic analysis by constructing models that explain how the market economy interacts with nature and knowledge.”, the Royal academy of Economic Sciences tweeted.

Paul Romer 63, is a Professor of Economics at New York University. Before this, he worked as a Chief Economist and Senior Vice President at the World Bank. He completed his undergraduate studies from Massachusetts Institute of Technology and a master and PhD from University of Chicago where his dissertation was supervised by Robert Lucas Jr. and José Scheinkman. Romer is well-known for his endogenous growth theory.

William Nordhaus 77, is a Professor at Yale which is also his Alma mater. He is said to be well-known for his work in climate change and economic modelling. Nordhaus was supervised by Robert Solow during his PhD at Massachusetts Institute of Technology (MIT). He is a prolific author who co-authored the famous book of Economics which was earlier written by Paul Samuelson.

18 INGOs asked to wind up activities within 60 days in Pakistan

On October 02, Tuesday, the government of Pakistan issued notices to 18 international non-government organizations (INGOs) to wind up their activities in the country within 60 days. These INGos include:ingos

  • Center for International Private Enterprise (CIPE), US
  • Internews Network, US
  • Pathfinder International, US
  • Central Asia Education Trust, US
  • American Center for Int’l Labor Solidarity (Solidarity Center), US
  • World Vision, US
  • Catholic Relief Svc (CRS), US
  • Plan International, US
  • International Relief and Development Inc, (IRD), US
  • International Alert, UK
  • Saferworld, UK
  • ActionAid, UK
  • Stitching BRAC International, Netherlands
  • Rutgers, Netherlands
  • Trocaire, Ireland
  • Danish Refugee Council (DRC), Denmark
  • Foundation Open Society Institute (FOSI), Switzerland
  • ISCOS, Trade Unions Institute for Development Coop, Italy

A letter sent to the country director of Action Aid UK stated that a special committee disapproved a representation filed by the Action Aid and therefore it has been asked to wind up the activities within 60 days. This organization may re-apply for registration after six months.

Action Aid UK, Plan International US, Rutgers Netherlands, International UK, Trocair, Ireland and World Vision US are among the organization which has a countrywide presence in the country and hundreds of employees are working with these organizations.

2018 Nobel Peace Prize Winners – who are they?


On Friday, October 05, the Nobel Peace prize was jointly awarded to Nadia Murad and Denis Mukwegi. Nadia 25, is an Iraqi Yazidi young lady who was sexually assaulted by IS militants for three months while Denis 63, is a gynecologist who spent decades in treating the rape victims in Congo.

Nadia Murad was born in Sinjar, northern Iraq in a minority Yazidi community. She was hardly twenty one years old when the IS militants abducted her with other Yazidi people, mostly women. Many of her family members including her mother and brothers were killed. She was among the girls who were used as sex slaves for more than three months. She managed to escape when the guard mistakenly kept the house unlocked. She then moved to a refugee camp and managed to flee to Germany. When the journalists approached her in the refugee camps and asked about the time she spent with IS militants, she told them that she was sexually assaulted. The journalists asked her whether they can write her story with changing her name, she told them that it shouldn’t be changed. Instead, the story should be written with her real name so that the world would know that how the victims are being treated by the IS militants. In an interview with the Guardian she said, “I was an Isis sex slave. I tell my story because it is the best weapon I have.” She later moved to Germany with other refugees where she took an initiative to create awareness about human trafficking.

Denis Mukwege was born in 1955 in Congo. He studied medicine and during the Congo war he treated some women who were raped by the militants. After witnessing the cruelty in the form of injuries in rape victims, he dedicated his services to treat the women who were raped in the wars. The war mongers hated him for his service for the humanity and attacked him in 2014 in which his guard was shot dead but he managed to flee. This made him move to Europe. In his absence, people suffered a lot and demanded the international community to send him back. When he came back to his country, he was received by thousands of people who walked with him for more than 20 miles to drop him at his residence. He is said to treat the patients under high security.

“This #NobelPrize is a recognition of the suffering of women victims of rape and sexual violence, the need for a just reparation in their favor and the hope to draw a red line against the use of rape in armed conflict.:”, he tweeted.  

He has received many prizes for his service to the wartime sexual victims.

Photo Courtesy: BBC


10 Skills That Will Give An Immediate Boost To Your CV

Many people dream of a perfectly written CV and cover letter. Start making your dream a reality with these 10 skills that will set you apart from the competition!

Microsoft Office Proficiency

Most people have heard of Word, Excel, Powerpoint, and Outlook, but do you know how to use them? MS Office Suite is used by 80% of companies worldwide. It’s simple, popular and productive. Among the series of MS applications, the top ones are – Excel for spreadsheets, Outlook for email, Powerpoint for visual presentations, and Word for desktop publishing.

Foreign Language Ability

Open the door to endless possibilities by learning a new language. Studies show that up to 30% of vacancies in administrative and clerical roles go unfilled every year due to shortages in foreign language skills. Adding foreign language skills to your résumé not only helps you stand out, but improves your employability too.

Leadership Capability

The ability to lead people and teams is an important skill to add to your resume. Including leadership skills in your CV indicates to a potential employer that you can help a business thrive, especially if it’s not a leadership role you’re applying for. Highlighting skills like taking initiative and team management skills will help you make a positive impression, and show that you like to exceed expectations.

Creative Thinking

Being innovative and thinking outside the box communicates that you’re more likely to find solutions to challenges. Demonstrating your ability to be a creative thinker is important and common for all jobs. If you don’t have a solid creative-thinking experience, then think about how your creative nature has helped you in your personal life and how it will be an asset in your job role. Drive creativity through your CV.


Learn basic tech and design skills for success. HTML and CSS are the two building blocks of website development and in-demand skills for most jobs. For instance, while they are a must-know for a front-end developer, they are an important ‘additional’ skill for a digital marketer or blogger in this technology-driven age.

Communication Skills

There’s little doubt that communication is a skill you will always need. Whether writing an important business email, or interacting during a social gathering, effective communication is a non-negotiable life skill. Adding this to your CV and highlighting it during interviews will help you build a solid impression in the mind of your interviewer.


The ability to be a team player is essential in almost every industry and job. From collaborating with your team for everyday tasks, to managing conflicts in the workplace, being a team player is a crucial skill to showcase on your CV.

Design Skills

Design your way to your dream career. Be it for web designing, or a career in graphic design, or even as a marketer or sales executive, basic design skills is a key ingredient that is required to master a job role in a visual world. Adding skills on your CV like Adobe Photoshop, Design Thinking and Graphic Designing will truly set you apart.

Presentation Prowess

Effective business presentation skills can set you apart from the competition. Giving an interactive and easy-to-understand presentation with a focus on strong communication is a must-have skill for almost all jobs. From basic PowerPoint presentations to using various creative aids, your effective presentation skill is a must-mention skill on your CV.

Analytical Ability

Identify, analyse, measure, and report. Analysing and measuring data is essential to understand the success of the business. Analytical tools like Google Analytics and R Programming are important for job seekers to learn, understand and showcase.


Courtesy: Alison Marketing