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Bringing Back Science into “Data Science”

Bringing Back Science into “Data Science”

Far from the conventional science disciplines, like physics or mathematics, Data Science is a budding discipline: which means there are no proper definition to explain what data science is and what role it does play.

Nevertheless, the internet is full of working definitions of data science. As per Wikipedia, Data Science is

(an) interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics.

To that note, a very important aspect is left behind in this explanation: Data Science is a science first, which means a proper scientific method should be devised to tackle different data science practices. By scientific method, we mean a healthy process of asking questions, collecting information, framing hypothesis and analyzing the results to draw conclusions thereafter.

Go below, the process breakup is as follows..

Ask questions

Start by asking what is the business problem? How to leverage maximum gains? What ways to implement to increase return on investment? The finance industry takes help from data science for myriad reasons. One of the most striking reasons is to enhance the return on investment out of marketing campaigns.

What Sets Apart Data Science from Big Data and Data Analytics – @Dexlabanalytics.

Collect data

A predictive modeling analyst has access to vast data resources, which eventually makes the entire research and gathering data process much less complex. However, it is only in theory, because rarely data is stored in the desired format an analyst wants, making his job easier.

Data Science – then and now! – @Dexlabanalytics.

Devise a hypothesis

After getting to the heart and soul of the problem, we start to develop hypotheses. For example, you believe your firm’s profit is leveraged by an optimistic customer reaction towards your product quality and positive advertising capabilities of your firm. Through this example, we explained a nomological network, where you are in a position to infer casualties and correlations. While dealing in Data Science, assessing customer perception is very crucial, and so is the analysis of financial datasets.

Data Science: Is It the Right Answer? – @Dexlabanalytics.

Testing and experiments

Formulating a hypothesis is not enough; a predictive modeler relies on statistical modeling techniques to forecast the future in a probabilistic manner. Keep a note, this doesn’t result in indicating “X will occur”, instead it refers “Given Y, the probability of X occurring is 75%.”

Any proper experiment includes control groups and test, meaning a modeler when preparing a predictive model should divide the dataset so as to ensure availability of few data for testing predictive equation.

Now, if we talk about marketing – consider logistic regression. It offers a probability whether a binary event of interest will take place or not.

Enroll in an R Predictive Modelling Certification program to go through the mechanics of this problem. Reach us at DexLab Analytics.

Tracing Success in the New Age of Data Science – @Dexlabanalytics.

Evaluate results and infer conclusions

Now is the time to make a decision: do you prefer the quantitative approach? As social media is totally unstructured, the qualitative approach needs to be implemented using Natural Language Processing, which can be a tad difficult. Now, how about making a longitudinal analysis, while transforming data into time series? Do all these questions rake your mind? Yes? Then you are on the right track.

Keep Pace with Automation: Emerging Data Science Jobs in India – @Dexlabanalytics.

Reporting of results

This is the final battle scene for all predictive modelers. It calls for all the documents, based on which a modeler made his decision during the development process. All the assumptions taken have to be identified and highlighted beside the results.

And with it comes the end of our Science in Data Science process!

For more interesting updates and blogs, follow us at DexLab Analytics. Opt for our impressive Data Science Courses in gurgaon and lead the road of success!


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The Math Behind Machine Learning: How it Works

The Math Behind Machine Learning: How it Works

It is evident that in the last few months, we have had several people showcase their enthusiasm about venturing into the world of data science using Machine Learning techniques. They are keen on probing the statistical regularities and building impeccable data-driven products. but we have made an observation that some may actually lack the necessary mathematical knowledge and intuition to get the framework for achieving results with data. And this is why we have decided to discuss this lacking through our blog.

In the recent times, there has been a noticeable upsurge in the availability of several easy-to-use machine and deep learning packages such as Weka, Tensorflow, scikit learn etc. But you must understand that machine learning as a field is one that has both statistical concepts, probabilistic concepts, computer science and algorithmic concepts to arise from learning intuitively from available data and also is about determining the patterns and hidden insights, which can be used to build intelligent applications. While still having the immense possibilities of Machine Learning and Deep Learning which is a thorough mathematical understanding of many of these techniques which is necessary for a good grasp of the internal workings of algorithms to achieve a good result.

Enrol in the most comprehensive machine learning course in India with us.

Why we must think about the math?

To explain why it is necessary to behind the scenes into the mathematical details of Machine Learning, we have put own a few important points:

  1. To choose the right algorithm which will include giving considerations, to accuracy, to the right training time, complexity of model, number of parameters and the number of features.
  2. To choose parameter settings and to validate the strategies
  3. To indentify the under-fitting and over-fitting by understanding the bias-variance trade off.
  4. For acquiring ample confidence about the interval and uncertainty

 The level of math one will need:

The primary question when one tries to understand an interdisciplinary field such as Machine Learning, is the amount of math needed and the level of math needed to understand these techniques.

The answer to this question is not as simple as it may seem and is multidimensional which, depends upon the level and interest of the individual. Research conducted in these mathematical formulations and theoretical advancements for Machine Learning is an ongoing process and a few researchers are already working on few more advanced techniques. However, we will state the least amount of math that is a must have skill for being a successful Machine learning Engineer/ Scientist is the importance of each and every mathematical concept.

Linear algebra:

This is the math skill to have for the 21st century. One must be well-versed with the topics of Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Eigendecomposition of a matrix, LU Decomposition, QR Decomposition/Factorization, Symmetric Matrices, Orthogonalization & Orthonormalization, Matrix Operations, Projections, Eigenvalues & Eigenvectors, Vector Spaces as these norms are absolutely necessary for the understanding and the optimization methods for machine learning. The best thing about linear algebra is that there are a lot of online resources.

Probability theory and statistics:

Machine learning and statistics are not too different a field. And in reality some people have actually defined Machine Learning as “doing statistics on a Mac”. A few fundamentals that are a must have for machine learning are – Combinatorics, Probability Rules & Axioms, Bayes’ Theorem, Random Variables, Variance and Expectation, Conditional and Joint Distributions, Standard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian), Moment Generating Functions, Maximum Likelihood Estimation (MLE), Prior and Posterior, Maximum a Posteriori Estimation (MAP) and Sampling Methods.

Multivariate calculus:

Differential and Integral Calculus, Partial Derivatives, Vector-Values Functions, Directional Gradient, Hessian, Jacobian, Laplacian and Lagragian Distribution are some of the necessary topics necessary for understanding ML.

Data Science Machine Learning Certification

Algorithms and Complex Optimizations:

In order to realize the computational efficiency and scalability of our Machine Learning Algorithm and for exploiting the sparsity in the dataset, this concept is necessary. One must have knowledge of data structures such as Binary Trees, Hashing, Heap, Stack etc, and Dynamic Programming, Randomized & Sublinear Algorithm, Graphs, Gradient/Stochastic Descents and Primal-Dual methods.

A few other mathematical skills that are often necessary for understanding ML are the following Real and Complex Analysis (Sets and Sequences, Topology, Metric Spaces, Single-Valued and Continuous Functions, Limits), Information Theory (Entropy, Information Gain), Function Spaces and Manifolds.

Machine learning training in Gurgaon from experts with in-depth instruction on math skills is offered at DexLab Analytics. Check out our Machine learning certification brochure for the same at the website. 



Interesting Statistics of Employment: 5 Figures

Interesting Statistics of Employment: 5 Figures

It is a common sight to see the old and young talking about the job market that is going through a slump, regardless of the time or the economic conditions of the country; this picture usually is accompanied with some “cutting chai” at tea stalls on busy streets or cool cafes at the malls with the slurp of espresso with a tiny straw where the average upper-middle class youth talk about their first-world dreams while breathing progressive third-world air.

But is that really always the case? Data management or statistical analysis as we have established several times before, is sending the job market into hyper-drive, attracting millions of MNCs into the Indian soil and populating the job search portals with millions of opportunities in data.  But dare we only make statements, we are statisticians and we know that numbers do speak louder than simple statements.

So, in keeping with our love for figures and facts backed by data, DexLab Analytics has compiled a list of interesting statistics about the job market and the process of hiring.

#1 Each and every major corporate job position attracts a minimum of 250 applications!

Out of all these applications only 4 to 6 resumes get shortlisted and are called for interviews. Out of these 4 to 6 people only 1 lucky candidate is selected.

#2 Every job seeker takes into account 5 factors before accepting the position at a firm.

They are –

  • The company culture, values and overall work environment
  • Distance, ease of commute, location
  • Prospects of maintaining work/life balance
  • Growth prospects in career and
  • Pay package and compensation.

#3 Almost 94 percent of sales personnel revealed that base salary is the most important determining factor in the compensation package for them.

But 62 percent of sales personnel say that commission is the most important element.

#4 Out of 3 employees at least 2 say that most employers do not do or do not know how to use social media platforms for promoting job openings.

And 3 out of 4 employees also believe that most companies and employers do not know how to promote their brand on social media networks as well.

#5 Social media platforms are used to search for jobs by 79 percent of jobseekers.

This figure rises to 86 percent for younger job seekers who are in their initial 10 years of job search.

To learn more about statistical analysis and for Data analyst certification in Gurgaon drop by our website at DexLab Analytics.


Interested in a career in Data Analyst?

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To learn more about Machine Learning Using Python and Spark – Enrol Now.
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