I definitely want to do original research and forward the common knowledge of the developer space. Might as well get a PhD while I'm at it.
Still I've been shopping for a Master's degree. A lot of companies offer some type of leadership development / mentoring. If you combined that with Corsera work I'm sure I could match the curriculum of most MBA programs. I could create a more tailored program that would be preferable*. That is obviously not the same as a master's. But.
Let's look at the value gap between the two. Let's assume simple numbers. I make 50k and by getting a masters I expect to increase my earnings to 100k. Even if I finance my education, paying three times as much over time, It is a 90k investment over several years with a return of an additional 2.1 million in earnings over the course of my working life. So. No brainer right?
Well. No. I know plenty of MBAs starting around 60k. If you invest the same 90k in education for and additional 10k a year then you will break even for ten years to pocket an additional 234k over your working life. Valuing the additional time and effort, I don't think it is worth it.
With Corsera there is considerably less risk. It's $50 a month (about $1800 for three years). Corsera offers less risk, but at a cost. The market values an MBA more than comparable course work. So all things equal an MBA candidate should win out over someone who completed coursework on Corsera.
There is the argument that schools don't add value to employees. They are tools we use to signal employers. So if you have enough drive does it matter? I think it does. School will open doors.
There is also the marginal utility of additional earnings. Once you get over 76k the marginal utility of a dollar starts tapering off. Basically, you will have nicer stuff but you will not be any happier. More people are starting to realize that. Big house nice car is a dated value.
My question. Is an MBA 16 times better than online coursework? It costs 16 times as much. No doubt it is better, but 16 times better?
*Any program would have to include some type of social networking component.
Difference in Difference estimation is a linear regression methodology used to analyse the effect of some event in time (the treatment) by comparing results over time. The idea is to compare data before and after the treatment. If the treatment was effective then you will find material differences in the outcomes for the target variable, or Y.
Before and After. This is a basic comparison of means for the time periods before and after treatment. For this specification we assume the target (Y) in the post-treatment period is equal to the target (Y) in the pre-treatment period in the absence of treatment. Thus, any change is attributable to the treatment.
Difference in Difference estimation is the natural extension of the before and after analysis is to include a control group for comparison. The difference in difference specification allows us to do this. We can compare Y as we did for the before and after analysis. We can also compare Ys between treated and untreated groups. To complete a difference in difference specification we use two dummy variables that partition the sample into four groups. The first dummy variable, treatment, partitions the sample in two halves based on their treatment status. The second dummy variable, post, partitions the halves in quarters based on the time period. We then interact treatment and post (Post*Treatment); the coefficient on Post*Treatment estimates the statistical difference in Y.
Y = B0 + B1*Post + B2*Treatment + B3*Post*Treatment.
It is easy to get overwhelmed or even lost because of the many niches within the leadership & business development genre. I read a lot in this space. The more I read the more I feel I need to read. It can be discouraging. It is easier for you to digest if you break it down into chunks. Here are four to start: building relationships, business acumen, self awareness, & organizational strategy.
Building Relationships. Do you want to be my friend? Sometimes that is all it takes. My kids do it all the time. Building leadership starts with positive and effective relationships. This is a core competency.
Business Acumen. You need to be able to do something, attain goals with minimum guidance. Otherwise you are just hanging out. This includes the competencies of teamwork, performance & motivation. Although it also encompasses technical skills, managing work, and industry knowledge.
Self Awareness. Self awareness is the bud that flowers into leadership. If you think of these as stages then this is where a top performer starts to become a leader. That is why a lot of training starts here. Self awareness includes the competences of vision & values, sustainability, resilience, and emotional intelligence.
Organizational strategy. The ability to set the vision and direction for the long term success of the organization. Its comprised of the following competencies: political acumen, awareness. change, culture.
It's impossible to tackle all of them at once. That is the point of chunking it down. Pick one. Make a four to six week plan to focus on a skill in your chosen competency. Work to actively change your behaviors and habits. Habits drive change. It takes 4 to 6 weeks groove the behavioral and neurological pathways of the new habit you want to create. In a year to a year and a half you can make meaningful progress in each of the 12 competencies.
Create a masterminds group with professional peers. Focus: What is working?What needs improvement? Share challenges and successes. Support each other with suggestions, encouragement, ideas for improvement. Network to create mutually beneficial opportunities.
Attend leadership development programs. Some ideas on where to look for programs: at work, local universities, non-profits, LinkedIn Learning, Coursera.
Form a leadership development group. Read a book or take a course and come together to discuss implementing the changes.
Why talk about leadership on a blog about data? We have not talked about data or decision making. I don't have a lot of time to write. I debated including leadership as a thread of this blog. I decided the topic merited inclusion.
These 12 competencies are inputs that go into decisions. For a data driven organization, a data savvy manager, to make the best use of data an understanding of leadership theory is needed. The theory informs a leader of the right questions to ask when mining data.
The Lord will never permit me or any other man who stands as President of this Church to lead you astray. It is not in the programme. It is not in the mind of God. If I were to attempt that, the Lord would remove me out of my place, and so He will any other man who attempts to lead the children of men astray from the oracles of God and from their duty.
I used to think this meant that the the leaders of the church were infallible: incapable of making a mistake or being wrong. It's easy to be snarky and point out that (of course) they are human, but to actually point something out as a mistake well that . . . that's pretty much apostasy, right? At the very least it's a demonstrable lack of faith.
<sarcasm> I find your lack of faith disturbing because you already know only the wicked take the truth to be hard </sarcasm>.
Can we be lead astray?
What does it mean to be lead astray? The work and glory of God is the immortality and eternal life of man (and woman). It isn't . . . to be right. It isn't to be above reproach. It isn't freedom from embarrassment.
Did Joseph Smith doubt that he had been lead astray when he wrote, "O God, where art thou? And where is the pavilion that covereth thy hiding place?"
Probably not. But if Joseph Smith could suffer torment, isolation, and martyrdom I think public embarrassment is within the scope.
<opinion> The Lord will not risk the immortality and eternal life of his sheep, but he will not protect us from feeling sheepish in the short term. </opinion>
President Hinkley experienced it in a way few ever have or will. Elder Gordon B. Hinckley was called as a third Counselor to the First Presidency on July 23 1981, when President Spencer W. Kimball as well as his two counselors were unable to attend to all their duties. By 1984, Hinckley was the only publicly active member of the First Presidency. After Kimball's death, Ezra Taft Benson became President of the Church. Benson named Hinckley his first counselor and Thomas Monson his second. In the early 1990s, Benson developed serious health problems, removing him from public view. Hinckley and Monson carried out many of the duties of the First Presidency until Benson died in 1994. After Benson’s death, Howard W. Hunter became President and retained Hinckley and Monson as counselors in the First Presidency. Hunter died nine months later. At this point in 1995, Hinckley assumed the mantle of Prophet. Hinkley knew the limits of those called to the presidency better than most. His thoughts:
I have worked with the Presidents of the Church from President Heber J. Grant onward. … I have known [their] counselors, and I have known the Council of the Twelve during [these] years. All of these men have been human. They have had human traits and perhaps some human weaknesses. But over and above all of that, there has been in the life of every one of them an overpowering manifestation of the inspiration of God. Those who have been Presidents have been prophets in a very real way. I have intimately witnessed the spirit of revelation upon them. The Lord refined and polished each one, let him know discouragement and failure, let him experience illness and in some cases deep sorrow. All of this became part of a great refining process, and the effect of that process became beautifully evident in their lives.
2015 October Conference. President Monson is concluding his thoughts when his strength began to fail putting him at risk of a fall. Why doesn't someone help him? With the lights dimmed, very few can see the whole picture as the prophet concludes his remarks. Michelle Cope was there. Her story is a great example of how the Lord carries us at times. She gives the following account:
Most of you probably did not see what was happening behind President Monson at the end of his talk. I was on the floor, just a few rows from the very front of the Conference Center with a clear view of the scene. You might have noticed that President Monson really struggled to finish the last couple minutes of his talk and especially the last 30 seconds. I was afraid for him. I thought he might faint, pass out, or something worse.
And then, my heart melted when I saw behind President Monson was President Uchtdorf – on the edge of his seat, almost half-way standing up, with his arms stretched out, ready to catch the Prophet at any moment if he fell. You could see the worried expression on President Uchtdorf’s face as well as focused determination. He was on high alert and ready to catch him. As soon as President Monson said “Amen”, President Uchtdorf was immediately at his side and carried him back to his seat, safe and sound.
President Monson teaches us, both in word and example, when we are on the Lord’s errand, He promises, “I will be on your right hand and on your left, and my Spirit shall be in your hearts, and mine angels round about you, to bear you up.”
When your life is difficult, when affliction leaves you wobbling and short of breath, don’t worry if no one is by your side, because He has your back.
Having the answers is not the mark of faith. It is only by asking questions that we gain our own greater understanding.*** I find these stories from the lives of two presidents build my faith in the leadership of the church as well as in myself. It is on you to work out your own salvation. The Lord will not put our salvation at risk but, you might by holding an expectation of infallibility in our leaders.
*** Answers are not necessarily included.
All data is a combination of signal and noise. Signal represents valuable consistent relationships that we want to learn. Noise is the random correlations and stuff that will not occur again in the future. The combination of signal and noise takes on familiar patterns or shapes that we can use to build a model.
Models can consider varying degrees of signal and noise. On end of the spectrum is the non-model which disregards signal & noise. Consider this common upsell question.
“Would you like fries with that?” This approach requires no model. It disregards signal and noise, rigidly canvassing everyone regardless of who they are or what they ordered:
“I’ll have a salad, hold the dressing. And a bottled water.”
“Would you like fries with that?”
“Carbs? Uh, no thank you.”
On the other end of the spectrum are flexible solutions that consider signal and noise. The predictions gathered are influenced by every piece of data, including outliers which can (and do) skew outcomes. These solutions can be more damaging than using no model at all. Such models lose predictive ability for new data because they are too tightly bound to their original training data. This is called overfitting.
This post is a continuation of my last post where I went over how machine learning fits into the scope of data science. This post goes a step further to talk about how we use machine learning to separate signal from the noise. There are many machine learning algorithms. Think of them as tools in a tool box. Data scientists use these pre-built algorithms to tease models from their data sets.
A handful of these tools are based on classical statistical methods which makes them easy to interpret. If the model is being used to aid a human to make decisions it’s a good idea to develop the model with these classical methods:
There are more options If there is no human involvement in the decision process; think of Netflix making a recommendation, no one reviews the recommendation before you get it. If there is no human element or if there is a high tolerance for opaque black box methods there is an additional group of modern machine learning algorithms:
Discussing each of these is outside the scope of this post. But if you are interested in learning more, make sure to subscribe to my email list for data savvy professionals and get a copy of “Bull Doze Thru Bull.”
Take away. If you are evaluating a machine learning project a great question is to ask about the algorithms that were used. LASSO & Random Forest are as close to out of the box all purpose tools as you will find so they are quite common. The classical methods are a conservative choice. The modern machine learning methods are really black box solutions which means they probably tried all of them and went with the tool that performed the best in testing.
So . .. are you a BI guy?
I get that often and the answer is yes, yes I am. In actuality the answer is, “well . . . sort of. Not really. It’s more machine learning.” If I get into it there is a variety of questions that start with: “What’s the difference between …”
What’s the difference between business intelligence and machine learning? Even if you google these terms it’s hard to find a good definition. For sure you will find definitions, but not meaning and context for a never ending list of terms in a jargon rich field.
Quoting a Google employee, “Everything at the company is driven by machine learning.” What does that mean exactly? Is that big data? What about data mining? How does that fit in? Is this all just fancy jargon for old school econometrics and statistics?
In the next 3 min i am going to take on the job of getting you up to speed on what all these terms mean in relation to each other. It isn’t enough to have a list of definitions, you need to understand context. That is what I will give you here. Context.
When you think about Business Intelligence you might confuse it for Business Analytics. Business Intelligence runs the business. Business Analytics changes the business. Intelligence directs process. Analytics directs strategy. Intelligence focuses understanding for today. Analytics focuses planning for tomorrow.
BI is real time access to data. Reporting. BI identifies current problems, solutions, and enables informed decision making. Business Analytics explores data: statistical analysis, quantitative analysis, data mining, predictive modelling among other technologies and techniques to identify trends and make predictions. But the two areas are merging as evidenced by these headlines:
I’ve heard machine learning described as the brains behind AI. Machine learning is the subfield of computer science that gives "computers the ability to learn without being explicitly programmed." I think of Machine Learning as a collection of pre-built algorithms for building models to predict future outcomes. Business analytics is about using those models on the execution side, putting insight into context and making things happen. In my last post I talked about the difference between data science and data savvy. Business analytics requires data savvy while machine learning is a component of data science.
Data Science deals with structured and unstructured data. In principle, everything that relates to data cleaning, preparation and analysis lies within the scope of Data Science. Data science is interdisciplinary requiring training in statistics, computer science, and industry. Solo practitioners with specialization in all three areas are rare so it is common to have data science teams: a data savvy manager, an econometrician, & a developer trained in machine learning.
Traditional Research. If you know anything about analytics (or statistics) you are probably familiar with regression: “ordinary least squares”. If not I highly recommend reading the book, Freakonomics. Regression is a mathematical way of drawing a straight line that most closely fits a scatterplot of data.
Regression is the basis for econometrics which is squarely found in the arena of traditional research. As you can see on the venn diagram traditional research blends classical statistics with industry knowledge. The emphasis of traditional econometrics is to use statistical tools to determine causal relationships in data. An econometrician wants to be able to tell why something is happening in the data. They want to tell a story about why you see correlations. And they do that using different variations of the regression technique.
Software development. We all have apps that make our lives easier & more entertaining. A relative few are lucky enough to earn a living developing and/or supporting software, SaaS (Software as a Service). Traditional software development makes processes more efficient. Most of development exists around this. This field requires both Computer Science (coders) as well as industry knowledge. This space is marked by partnerships between clients and their SaaS providers.
Developers will spend a lot of time and resources understanding their client's existing process to build solutions around industry best practices.
Machine Learning. Which brings us back to machine learning, which is probably not as familiar as software development or traditional research. When a developer uses machine learning, what does that look like? It starts with a dataset. As is the case with traditional research the first step is to prepare the data for analysis. Data prep, munging or data wrangling, as it is called is the most time intensive step. The second step is to separate the data into two parts. Two thirds to 70% of the data is used for training the model.
One third to 30% is saved for testing the model. A machine learning modeler has a variety of tools at their disposal to build a model of relationships based on the training data. The modeler will then make predictions about the test data based on this model. The more accurate the predictions, the better the model.
At this point you should have a clear idea of what data science is: a blending of machine learning, traditional research, and software development to create predictive models. To contrast BI focuses on dashboards and reporting for the here and now. BI focuses on process. DS focuses on strategy. Data science requires a variety of advanced skill sets which makes data science teams quite common (and full stack data scientists quite rare). Business analytics on the other hand requires data savvy: a survey level understanding of data science topics with the purpose of putting these models to good use by executing on business goals.
Among these topics a data savvy professional should be familiar with is an understanding of machine learning and the strengths and limitations of the more common algorithms used in machine learning. If you are interested in learning more, make sure to subscribe to my email list for data savvy professionals and get a copy of “Bull Doze Thru Bull.” In my next post we are going to explore these topics and get under the hood with machine learning.
Seth Godin's Linchpin is for you. Your boss. Your team.
Linchpin is about leading, and change, and fear, and succeess.
You couldn't write this book ten years ago, because ten years ago, the economy wanted you to fit in. It took care of you . . . if you fit in. Now, the world wants something different. This book exposes a multi-generational conspiracy to sap your creativity.
What if you learned a different way of seeing?
A different way of giving?
A different way of making a living?
What if you could do it, without leaving your job?
(Or joining a network marketing scheme.)
A way . . . to contribute your true self and your best work.
Are you up for that?
McKinsey says there will be a shortage of data skills in 2018. Mckinsey predicts a shortfall in meeting the demand for 1.5 Million Data Savvy Managers. Savvy managers can make use of data on the execution side, putting insight into context and making things happen.
A major hurdle to iterating and improving strategic data driven decision making is people. Data analytics is pretty straight forward; i.e. math is just that, math. It's people (humans) that is the problem. Which means people (could that be you?) are the solution. Data science relies heavily on statistical computing. Scripts and math. Algorithms. If (1) you start with good data and (2) you have a competent data scientist conduct and interpret the analysis, you still need (3) to put those results into context; make something happen. Someone has to do! Teams (doers) need to execute on insights.
Listen. Understand the problems your team, senior, & mid-level managers are facing.
Ask great questions. Frame the problem into a set of questions that, if answered, direct action. Understand (& communicate) that decisions must be made once these questions are answered.
Understand data science. Take a survey level course on data science. LinkedIn Learning offers a course that you can get through in an afternoon. When you understand the process you can ask actionable questions that lend themselves to be answered with a data model.
Evaluate alternatives. Data often suggests multiple approaches; assemble the right team that can prioritize them.
Acknowledge and mitigate bias. Team members have (and use) inherent bias. Teams that manage GroupThink will naturally make better evaluations.
Catalyze change. Communicate and empower decisions throughout the organization. Building the architecture need for changes to take place.
These six skills are crucial to developing processes that:
(1) generate meaningful questions
(2) pose those questions effectively
(3) build understanding around data driven decisions
(4) create a culture that can implement those decisions.
Data Science requires rare (specialist) qualities:
(1) an ability to take unstructured data and find order, meaning, and value.
(2) Deep analytical talent.
Data Savvy doesn't.
To be a generalist, a data savvy manager, doesn’t. Data savvy doesn't require you to be a math expert,
learn more @ www.assume-wisely.com/data-savvy-manager
I just spent the last nine hours ringing my brain out. Every so often (when needed) I play hero. This feels like a hero moment. It's not. I actually under preformed because I was mugged. They took off with my time and focus. That is something to pay attention to. That is not sustainable. And it is definitely not scale-able. I am asking myself:
What just happened?
I have a theoretical throughput capacity. Individual heroism is great, but it doesn't scale. Neither do muggings. Like David Bowie says, "we can be heroes, just for one day."
For data to be useful track it consistently. Apples to apples indicates units as well. You can't track crates of apples if everyone else is tracking the actual apples. Data can identify economies of scale. Data can identify new opportunities but it helps if it is clean.
The Goal audiobook is still playing in my head, "What can we do to double current weekly throughput in the future?"
Load balance the week. If Mondays are particularly heavy ask yourself, what can wait for Tuesday? What can get shipped early? The Friday before? Shipping early is a big deal!
Double you ability to ship on any given Monday by load balancing to spread delivery across days. Track data consistently. Don't rely on heroes.If you want to better understand the principles I'm using to construct these thoughts check out the Goal audiobook.
By Rho Lall
1. What are you planning to read for the Summer Biannual Bibliothon?
2. What is your favorite genre to read in Summer?
Most of what I read could be pejoratively labeled, " the gospel of success". I like business non-fiction.
3.Where is your favorite place to read in the summer?
I prefer reading paperbacks by the pool or at the beach.
4.What is your favorite challenge done in the Summer Biannual Bibliothon?
Exercising my first amendment right to read a banned book.
5.What fictional character would you hang out in the summer if you could?
Come back to me on this one.
6.What are your plans for summer?
Going to N.Y.C. & D.C.
7.Do you have summer reading playlist,If not what would be on it?
I am re-reading my list of best business book ever for working professionals.
8.What is your favorite summer movie?
Live Free or Die Hard. The helicopter scene is the best!
9.What book do you read every summer,if not what thing do you do every summer?
B.B.Q. I smoke meat.
10.What other book tags are you planning to do this summer?
5.What fictional character(s) would you hang out in the summer if you could?
That is a hard one. I don’t read a lot of fiction so the list of potentials is literally, Harry Potter, & The Hunger Games. I am going to go with the small group of people who know what Covfefe’ means.