What are the 10 most in demand skills in 2017?

This post originally appeared as an answer to a question on Quora

“This is all useless,” I thought.

I’ve had this scenario occur several times throughout the process of formal schooling.

I felt it in high school while learning the “SAS” rule to compute angles for a triangle.

I felt it in college when I had to listen to my professor — who never had any business experience except for being a business professor — read the bullet points of his power point presentation word for word.

I have a 15 month old daughter and I fear she will feel the same things I’ve felt.

Much of what school teaches us has little to nothing to do with the real world. This isn’t a mystery. Many of us know there are problems with the current education system.

As far as systemic changes are concerned, there are too many layers of red-tape, and fundamentally changing the system will cause a lot of bureaucrats to have lighter pockets. I’m not crossing my fingers expecting change any time soon.

I decided to play a different game with my life. While the majority of people follow the prescribed program of “Go to school, take tests, graduate, and enter the employment funnel with no sense of meaning, desire, or intrigue,” I have and will continue to develop the skills that make a difference in the real world.

Allow me to put this into context first. I believe these skills are the top 10 in demand skills for people who want to choose themselves. If you’re content to wander aimlessly through your career, jumping from company to company, adding bullet points to your LinkedIn profile that read akin to “specialized in optimizing synergistic solutions,” this ain’t the post for you.

But, if you want to do what that matters to you on a personal level and live a life that does the same, these tips might help.

  1. Self awareness - Frustration in life comes from a lack of self-awareness. Lack of self awareness is the reason why people end up in careers they hate. Someone attuned to their preferences, strengths, and desired outcomes doesn’t accidentally stumble into an awful career. First, determine what you’re good at. The process to do so is rather long, which is why I wrote two entire books about it. Second, figure out what’s led you astray. We receive subtle messages from the world telling us who we’re supposed to be, how we’re supposed to behave, and what the rules are. You have to unlearn all of that stuff and then relearn how to live a life on your own terms.
  2. The Ability to Learn - Warren Buffet once said, “The more you learn, the more you earn.” His partner Charlie Munger says, “The most successful people in the world aren’t necessarily the most talented, but they’re learning machines.” Here’s the thing. Most people end their education after they’re done with school. If you take even a little bit of time to increase your knowledge - by reading, watching videos, taking online courses, ect. - you’ll be miles ahead of everyone around you. Most people don’t even practice continued education in their own field or industry - let alone develop an eclectic knowledge of different subjects. If you have a base level of education in various disciplines, you have a tool belt you can use to solve different problems. Turn your brain into a “Swiss Army knife” and you’ll become indispensable because few people will have your array of knowledge.
  3. Bias towards action - The world is moving at a fast pace. Those who develop the skill of acting on the information they receive will reap rewards in the future. Knowledge isn’t power. The application of knowledge is power.
  4. Online Marketing - Career expert Penelope Trunk says having a blog improves your career success. Marketing expert Seth Godin believes everyone should write a personal blog daily. In today’s world, employers, potential business partners, or fans are going to Google you and check your social media profiles to learn more about you. The online presence has replaced the resume. You can use content marketing to display your expertise. People have received job offers, landed business, and gained publishing deals from simply keeping up with their online presence. Everybody has a brand and a media presence today — everyone — some people just have bad ones.
  5. The Ability to Come up With Ideas - Quora’s pound-for-pound champion, James Altucher, has dubbed this era in society “the idea economy.” New ideas are emerging everyday, and now is the perfect time to turn your ideas into reality. Testing ideas is now easier than it ever has been before. With the “gig economy” you can work with freelancers across the globe to come up with a “beta test” for a business idea that doesn’t cost much money. As an employee, if you’re working in a visionary and flexible company, they’ll let you test your new ideas. If the company doesn’t let you test them, you should quit. How do you learn how to come up with good ideas? By coming up with tons of bad ones. One of the most valuable tips I’ve learned from James is to come up with 10 ideas per day — about anything. Write down ten ideas each day on a chosen subject. 9 will be bad. 1 will be mediocre. Every once in a while, one will be great.
  6. Sales Skills - You’re a salesperson whether you like it or not. You’re selling when you’re at a job interview. You sell when you try to get a person of the opposite sex to like you. You sell when you’re trying to convince your kid to eat their peas. The ability to persuade is the umbrella with which all other skills fall under. You can have a great product, but without persuasion it won’t sell. You can have valuable skills to provide to the market place, but without persuasion you won’t land the best opportunities. Read the book Influence - the Psychology of Persuasion, because it will give you superpowers.
  7. Focus - Our attention has become more fragmented every day. We have our phones on our laps, while watching T.V., while having our laptop open. With so many distractions, the ability to do deep and meaningful work is decreasing at a time when this type of work is most needed. Cal Newport covers this in his book Deep Work. Deep work helps you make creative breakthroughs. It’s what gets you into the state of “flow” where you’re in the zone. According to the Deep Work theory, work done in a state of focus for a shorter period of time produces better result than work done in a state of distraction for longer periods of time. Many argue the 8 hour work day is too arbitrary, and that 4–6 hours of deep work per day would produce more output. Evidence continues to pile up about the negative impact multi-tasking has on our brain. If you lengthen your attention span, you can put in a level of work that stands out above the crowd, because your peers simply can’t focus long enough to create astonishing work.
  8. Patience - We want “six minute abs.” We want to “start a six figure business in six months.” Right now, we’re living in one of the most opportunistic periods of human history. You have more access to resources than ever before to help you build a life and career to your exact specifications. That being said, those resources don’t create a “cure all.” There’s no easy button for success in 2017. I’ve seen people fall prey to a lack of patience more times than I can count. They’re the type who start blogs, write two posts, and quit because they have no fans. They make excuses for why others are succeeding, when in reality, the only difference is the length of time put into building their career or business. As far as what it takes to develop patience, I don’t have the perfect remedy. For me, it’s an insatiable desire to accomplish my dreams. I know I’m going to die, perhaps sooner than later, and I keep that in the forefront of my mind while I work. There are days when I feel like doing nothing, but I remind myself that it could be my last day, and often I get back to work.
  9. Hustle/Grit/Determination/Persistence - You have to be hungry. This skill reminds me of a lyric from a Kendrick Lamar song - “Time will never wait on no man, society will never hold your hand.” Whatever you’re looking to accomplish in 2017 or beyond, nobody is going to hand it to you. This is why I’m against participation trophies for kids and helicopter parents/soccer moms. People are becoming too soft, protected, and idealistic. If you want to succeed in the real world, you have to accept that life will punch you in the face, and you have to get off the mat and punch back. Complaining isn’t a strategy. Like I’ve stated previously, there’s so much opportunity in the world for those willing to hustle for it. In the long run, things usually work out for the persistent. It was true in 2017 B.C. and it’s true now. Some skills are timeless, and the ability to persist is one of those skills.
  10. Social Skills - You can be the most intelligent person in the world, but if your social skills aren’t up to par, it won’t matter. You’ve heard the cliche, “It’s not what you know, but who you know.” The thing about cliches is they’re usually true. You can’t succeed alone. You need to build a large network of people so you can help each other. Being social makes people want to work with you, do business with you, and endorse you. You don’t have to be an extrovert, but find ways to connect with other people in a meaningful way. I once heard a great rule that went something along the lines of “treat everyone like they can get you press in the New York Times,” or something like that. The point was that you don’t know what people can do for you down the road. Don’t judge people on first appearances. If you go to a business meeting and you’re rude to the receptionist, that could get back to the person you’re meeting with and kill the deal. Treating other people well should be something you do simply because you’re a good person, but if you need an extra incentive, treating people well can lead to karma coming back your way when you need it most.

There you go! I hope that helped.

If you’re looking for ways to develop skills and reinvent yourself in 2017, you should read my new book, because it covers a wide range of techniques you might find useful.

Structural Equation Modeling Using SPSS AMOS

Structural Equation Modeling using SPSS AMOS

Learn structural equation modeling using SPSS AMOS in private and instructor led online courses at AnEc Center for Econometrics Research.

The course aims to develop a strong foundation of the theory and application of modeling relationships between observed and unobserved variables, path analysis and confirmatory factor analysis using structural equation modeling with SPSS and AMOS.

Enroll for SEM using SPSS and AMOS

Course Objectives

Structural Equation Modeling using SPSS AMOS is advanced level research course aiming to train PhD and MS students in Economics, Business, Finance and Social Sciences to develop modeling skills for analysis of latent variables and observed variables with multi-equation and endogenous covariates. The course also aims to use SPSS and AMOS at advanced level.

Enroll for the Course

Understanding the theory and practice of SEM
Developing the skills of modeling latent variables
Analysis of data using complex path analysis
Using SPSS and AMOS at advanced level
Writing effective research reports and thesis

Registration Fees

Structural Equation Modeling using SPSS AMOS is an online, private and instructor led course from AnEc Center for Econometrics Research led by Professor Anees Muhammad since 2010.

The course is designed to help the students in Economics, Finance, Business, Management, Psychology, Political Sciences, Social Sciences and Healthcare pursuing PhD and MS research. The course can be enrolled in private or in group. Private courses are recommended for students who need to use SEM during their writing of PhD thesis.


Private Course

Recommended for students writing PhD Thesis
For 2 Months Course
100% practical based lessons
Detailed lessons on theory
Complete supervision of thesis

Enroll for SEM Course

Public Course

Recommended for those who wish to master the use of SEM
For 2 Months Course
100% practical demosntration
Fully interactive video conference
Coursework based assessment

Enroll for SEM Course


Selected Course Contents for SEM using SPSS

These are only selected topics and we will cover the contents in more details so specific list of topics for each lessons and practical demonstration of SEM using SPSS AMOS

Introduction to SEM and Theory of SEM

Overview of linear structural equation modeling and fitting and evaluating structural equation models
Description of path diagrams, AMOS tools for SEM
, Building models using the GUI for AMOS
Details on specific types of structural equation models, Models for observed variables and Linear regression
Path analysis, Mediation and Moderation analysis, Exploratory and Confirmatory factor analysis

Validation of Estimated SEM Model and Tests

Full structural equation models, Latent growth curves
, Multiple group analysis and interpreting linear SEM results
Standardized results vs Unstandardized results, Direct, indirect, and total effects and Goodness-of-fit statistics
Modification indices, Score tests and Wald tests, Tests for multiple group analysis and fitting multilevel models
Generalized structural equation modeling vs simple structural equation modeling

Course Certificates

All certificates from AnEc Center for Econometrics Research are digitally signed can be verified online through the specific link given to the candidates or manually through emailing us back
AnEc Center for Econometrics Research (2)

Verifiable Digital Certificates

All certificate courses bear a verifiable certificates personally signed by the instructors and managing director of AnEc Center for Econometrics Research

LinkedIn Connected Certificates

The certificates can be digitally connected to our LinkedIn accounts and will display on your LinkedIn profiles for more international value.

Digitally Signed Certificates

All certificates are issued digitally and bear a digital signature, verification code from Accredible and AnEc Center for Econometrics Research, Attock.

Features of our Online Course in Structural Equation Modeling using SPSS AMOS

Personalized contents
Advanced course contents
Verifiable Certificates
One to one video conference
Completely Privately Discussions
AnEc Membership
100% practical demos
Weekly Live Lectures
Paid freelance projects
Instant Feedback and Answers
Each lecture is recorded
Research co-authorship

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Download SPSS and AMOS Here

Multivariate GARCH DCC Estimation

In this video tutorial, we will demonstrate the process of Multivariate GARCH DCC Estimation using OxMetrics 6. Multivariate Garch can be elaborate in the following written tutorial from: https://vlab.stern.nyu.edu/doc/13?topic=mdls

Definition of Multivariate GARCH

Consider n time series of returns and make the usual assumption that returns are serially uncorrelated. Then, we can define a vector of zero-mean white noises εt=rtμ, where rt is the n1 vector of returns and μ is the vector of expected returns.

Despite of being serially uncorrelated, the returns may present contemporaneous correlation. That is:


may not be a diagonal matrix. Moreover, this contemporaneous variance may be time-varying, depending on past information.

The GARCH-DCC involves two steps. The first step accounts for the conditional heteroskedasticity. It consists in estimating, for each one of the n series of returns rit, its conditional volatility σit using a GARCH model (see garch documentation). Let Dt be a diagonal matrix with these conditional volatilities, i.e. Di,it=σit and, if ij, Di,jt=0. Then the standardized residuals are:


and notice that these standardized residuals have unit conditional volatility. Now, define the matrix:


This is the Bollerslev's Constant Conditional Correlation (CCC) Estimator (Bollerslev, 1990).

The second step consists in generalizing Bollerslev's CCC to capture dynamics in the correlation, hence the name Dynamic Conditional Correlation (DCC). The DCC correlations are:


So, Qi,jt is the correlation between rit and rjt at time t, and that is what is plotted by V-Lab.


The estimation of one GARCH model for each of the n time series of returns in the first step is standard. For details on GARCH estimation, see garch documentation.

For the second step, which is the DCC estimation per se, V-Lab estimates both parameters, α and β, simultaneously, by maximizing the log likelihood. The standardized residuals are assumed to be jointly Gaussian. To ease the computation cost of estimating a vast dimensional time-varying correlation model, V-Lab uses a technique called composite likelihood (Engle et al., 2007).

The DCC model captures a stylized facts in financial time series: correlation clustering. The correlation is more likely to be high at time t if it was also high at time t1. Another way of seeing this is noting that a shock at time t1 also impacts the correlation at time t. However, if α+β<1, the correlation itself is mean reverting, and it fluctuates around R⎯⎯⎯, the unconditional correlation.

Usual restrictions on the parameters are α,β>0. Though, it is possible to have α+β=1; the conditional correlation is then an integrated process.

Variance Targeting

Notice that if we had written the DCC model in a fashion similar to the GARCH model:


we would have to estimate the matrix Ω also. That is, instead of estimating only two parameters, we would have to estimate 2+nn+12 parameters (it is not 2+n2 parameters due to the fact that Ω is a symmetric matrix). And then the unconditional correlation implied by the model would have been:


Instead of estimating Ω, notice that we actually substituted Ω by R⎯⎯⎯(1αβ) in the DCC formula, which is a much more parsimonious way of writing the model. This is called Variance Targeting, introduced by Engle and Mezrich in 1995, and it is a very useful technique when modeling vast dimensional time-varying covariance or correlation models.


The specific model just described can be generalized in two ways.

In the first stage, each GARCH specification used to standardize each one of the n return time series can be generalized to a GARCH(p,q) model (see garch documentation), where p and q can be chosen differently for each return time series, for instance, by Bayesian Information Criterion (BIN), also known as Schwarz Information Criterion (SIC), or by Akaike Information Criterion (AIC). The former tends to be more parsimonious than the latter. V-Lab uses p=1 and q=1 though, because this is usually the option that best fits financial time series.

In the second stage, the DCC model can be generalized to account for more lags in the conditional correlation. A DCC(p,q) model assumes that:


where p and q can be chosen, for instance, by information criterion. Again, V-Lab uses p=1 and q=1 though, because this is usually the option that best fits financial time series.


Bollerslev, T., 1990. Modeling The Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized ARCH Model. Review of Economics and Statistics 72: 498-505.

Engle, R. F., 2002. Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. Journal of Business and Economic Statistics 20(3).

Engle, R. F., 2009. Anticipating Correlations: A New Paradigm for Risk Management. Princeton University Press.

Engle, R. F. and J. Mezrich, 1995. Grappling with GARCH. Risk: 112-117.

Engle, R. F., N. Shephard, and K. Sheppard, 2007. Fitting and Testing Vast Dimensional Time-Varying Covariance Models. NYU Working Paper FIN-07-046.

Video On Multivariate GARCH DCC Estimation

Applied Statistics for Business and Management

Statistics for Business and Management professionals and academic researchers is of critical importance. Although there are specialized courses in Statistics for the students of Business and Management but none of the courses offer specialization in either, Stata, Eviews or SPSS and other computer packages. The current course introduces the application of Stata, Eviews and SPSS and other computer application while emphasis is laid on the development of theoretical background of the learners as well as with strong application of the techniques in Statistics for Business. The contents of the course offered to students include (but are not limited) to:
Basic Data Management and Presentation
Hypothesis Testing and Research Problems
Causal Relationships, Regression Analysis and Applications
Qualitative Data Analysis for Binary and Multi-chotomous variables

Also the students of the course would be provided additional support via other mediums like phone, SMS, email beside the traditional and favorite WizIQ!
This course will benefit the students of MBA, BBA and Doctorate students in Business and Management courses have to study and develop the skills of Quantitative Data Analysis. This course will master these students in their Statistical courses.
Course Highlights
Apply Statistical Techniques to real world data
Apply Data Analysis to relevant research problems
Wide range of contents which are usually required in broader contexts

The course package:
15 LIVE online classes
Duration: Two to Four Weeks (Negotiable)
All Days: 0500 PM to 1000 PM GMT (1000 PM to 0200 AM Pakistan Times)
The course outline:
Deterministic Data and Random Data
Multivariate Tables, Scatter Plots and D Plots
Measures of Association for Continuous Variables
Measures of Association for Ordinal Variables
Measures of Association for Nominal Variables
Estimating Data Parameters
Parametric Tests of Hypotheses
Non-Parametric Tests of Hypotheses
Statistical Classification
Multiple Regression
General Linear Regression Model
General Linear Regression in Matrix Terms
Multiple Correlation
Inferences on Regression Parameters
ANOVA and Extra Sums of Squares
Polynomial Regression and Other Models
Building and Evaluating the Regression Model
Principal Components
Dimensional Reduction
Principal Components of Correlation Matrices
Factor Analysis
Survival Analysis
The Exponential Model
The Weibull Model
The Cox Regression Model

Recommended books for this course:
Probability and Statistics, Fourth Edition by Morris H. DeGroot & Mark J. Schervish
SPSS for Introductory Statistics Second Edition by Morgan et al
A First Course in Business Statistics, Eight Edition by JAMES T. McCLAVE, P. GEORGE BENSON & TERRYSlNClCH
Note: Students must purchase these books themselves. These are not available as part of the course. Discount coupons may be asked from the teacher who would be happy to share if any available right now.

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Financial Econometrics Using Matlab: Two Weeks Course

Financial Econometrics using Matlab: A Two Weeks Intensive Instructor Led Course

Matlab is one of the important modeling, simulations and data analysis software widely used by academics and professional researchers in the area of Finance and Econometrics. The two Weeks long course is offered to graduate research students to enable them develop the strong skills of programming and analytics using Matlab

Enroll Now for Financial Econometrics using Matlab


Matlab is  matrix manipulation and computational engine that is commonly used for Financial Modeling, Simulations and Data Analysis loved by most of the specialists in the area of Econometrics and Finance. The current module is a two weeks long course  aimed at introduction of Theory and Application of Econometrics for Financial Analysis specifically using Matlab. The course will enable the enrolled students to independently apply their skills of Data Analysis and Modeling in the Areas of Econometrics and Finance. The course is equally important for the beginners and advanced learners in Finance, Economics and Financial Econometrics who wish to develop a strong hands on experience in econometric modeling and simulation. The main objective of the course is to introduce the theory and application of Financial Econometrics and how to use the powerful Matlab as a Mathematical Modeling Software.

Course ObjectivesCourse OutcomesCourse Contents

Course Objectives

The two weeks long course in Financial Econometrics using Matlab specifically aims to introduce the students and researchers of Finance and Economics to handle data from Finance and Investment quickly and produce high impact outcomes using Financial Econometrics and programming and analytics using Matlab.

Enroll for Financial Econometrics using Matlab

Understanding theory and modeling process
Practicing complex analytics approaches
Creating deep insights from raw financial data
Writing complex Matlab programs to analyse data
Writing high impact research reports and thesis

Course Outcomes

We expect that after completing the two weeks intensive, online and instructor led course in Financial Econometrics using Matlab course will enable our students to handle all kinds of financial and economic data to produce high impact research reports and thesis. The key skills we would ensure through our course include data analytics, report writing and Matlab programming.

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Developing independent learning skills
Critically evaluating econometric models
Practically estimating all econometric models
Programming and analytics in Matlab
Report writing and interpretation of all results

Course Contents

Financial Econometrics using Matlab is a two weeks intensive, private and instructor led online course, equally recommended to students, faculty and staff and professional researchers. The course builds on developing strong data analytics, statistical computing and financial modeling skills through the following topics and programming exercises in Matlab.

Enroll for Financial Econometrics using Matlab

Simple OLS, Autocorrelation and Heteroskedasticity
Unit root tests and spurious regression models
Autoregressive and Moving averages models
VAR, VECM, Cointegration, ARDL, ARDL, BreakPoints

Certification at AnEc Center for Econometrics Research

After completing the online course in Financial Econometrics using Matlab, successful candidates will be awarded Diploma Certificates from AnEc Center for Econometrics Research
Financial Econometrics using Matlab

Verifiable Digital Certificates

All the certificates from AnEc Center for Econometrics Research are digitally verified and can be manually verified through emails.

LinkedIn Connected Certificates

Our certificates are accredible and credentials and can be directly connected to your LinkedIn profiles from our certificate issuing system.

Published Coursework

We will publish your coursework details on a private page to verify your course learning and practical data analytics skills on AnEc System.

Why to enroll for a course at AnEc Research Center?

Since 2010, AnEc Center for Econometrics Research has been providing technical and skills based course in Data Analysis, Quantitative Research and Report writing. So far, we have helped more than 110 PhD students, 200+ MS students and 500+ academic and business researchers to complete their projects with our top econometrics specialists.

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Class based courses


Certificates issues


Research projects

Enroll for Financial Econometrics using Matlab
The Fees For The Course Is 450USD. If you need discount, mention your requirements in the message/comment box of the form. Thank you.
Download Matlab from Mathworks.com

Introduction to Matlab for Finance

Matlab has been one of the important Modeling, Simulations and Data Analysis software widely used by Financial Econometricians. The one week short course has been developed to introduce basic training in using Matlab for Finance enabling the registered students to independently apply their skills to Data Analysis and Modeling in the area of Finance. The course is equally important to the beginners and advanced learners in Finance, Economics and Financial Econometrics who want to develop a sense of modeling and analysis. The main objective of the course is to introduce the theory and application of Financial Econometrics and how to use the powerful Mathematical Modeling Software Tool, Matlab for analysis and simulation.

The course is planned in three sections:
·         Registration starts today: Today
·         Registration ends:November 14, 2016
·         Course Begins:November 15, 2016

Contents of the Course:
Selected Topics from the following will be discussed. Student has the choice to select one or many topics.
·         Solution of Stochastic Differential Equations
·         General Approach to the Valuation of Contingent Claims
·         Pricing Options using Monte Carlo Simulations
·         Term Structure of Interest Rates and Interest Rate Derivatives
·         Credit Risk and the Valuation of Corporate Securities
·         Valuation of Portfolios of Financial Guarantees
·         Risk Management and Value at Risk (VaR)
·         Value at Risk (VaR) and Principal Components Analysis (PCA)

All these topics will be discussed in context of Matlab

The course fee structure:
Individuals: 300GBP payable on registration for the course
Groups: 750GBP, payable after the invoice has been sent to the group leader

MatLab M-files, EBooks, Manuals, Example datasets and Lecture Recordings into DVD format will be provided upon enrolment for the course. EBooks and weekly lecture slides will be provided before commencement of each lecture. DVD recordings will be posted on the portal after the course completes and editing has been completed for improving quality

The course materials will be provided to those who register on https://elearning.aneconomist.com. Registration should be confirmed within 24 Hours by paying the course fee.  Only 10 Places are Available for live, online and person-to-personal online lecturing to maintain proper balance in discussion and participation.