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Time Series Analysis & Modelling with Python (Part II) – Data Smoothing

Posted on January 20, 2021January 20, 2021 by Dexlab

Data Smoothing is done to better understand the hidden patterns in the data. In the non- stationary processes, it is very hard to forecast the data as the variance over a period of time changes, therefore data smoothing techniques are used to smooth out the irregular roughness to see a clearer signal.

In this segment we will be discussing two of the most important data smoothing techniques :-

Moving average smoothing
Exponential smoothing

Moving average smoothing

Moving average is a technique where subsets of original data are created and then average of each subset is taken to smooth out the data and find the value in between each subset which better helps to see the trend over a period of time.

Lets take an example to better understand the problem.

Suppose that we have a data of price observed over a period of time and it is a non-stationary data so that the tend is hard to recognize.

QTR (quarter)	Price
1	10
2	11
3	18
4	14
5	15
6	?

In the above data we don’t know the value of the 6^th quarter.

….fig (1)

The plot above shows that there is no trend the data is following so to better understand the pattern we calculate the moving average over three quarter at a time so that we get in between values as well as we get the missing value of the 6^th quarter.

To find the missing value of 6^th quarter we will use previous three quarter’s data i.e.

MAS = = 15.7

QTR (quarter)	Price
1	10
2	11
3	18
4	14
5	15
6	15.7

MAS = = 13

MAS = = 14.33

QTR (quarter)	Price	MAS (Price)
1	10	10
2	11	11
3	18	18
4	14	13
5	15	14.33
6	15.7	15.7

….. fig (2)

In the above graph we can see that after 3^rd quarter there is an upward sloping trend in the data.

Exponential Data Smoothing

In this method a larger weight ( ) which lies between 0 & 1 is given to the most recent observations and as the observation grows more distant the weight decreases exponentially.

The weights are decided on the basis how the data is, in case the data has low movement then we will choose the value of closer to 0 and in case the data has a lot more randomness then in that case we would like to choose the value of closer to 1.

EMA= F_t= F_t-1 + (A_t-1 – F_t-1)

Now lets see a practical example.

For this example we will be taking = 0.5

Taking the same data……

QTR (quarter)	Price (A_t)	EMS Price(F_t)
1	10	10
2	11	?
3	18	?
4	14	?
5	15	?
6	?	?

To find the value of yellow cell we need to find out the value of all the blue cells and since we do not have the initial value of F₁ we will use the value of A_1.Now lets do the calculation:-

F₂=10+0.5(10 – 10) = 10

F₃=10+0.5(11 – 10) = 10.5

F₄=10.5+0.5(18 – 10.5) = 14.25

F₅=14.25+0.5(14 – 14.25) = 14.13

F₆=14.13+0.5(15 – 14.13)= 14.56

QTR (quarter)	Price (A_t)	EMS Price(F_t)
1	10	10
2	11	10
3	18	10.5
4	14	14.25
5	15	14.13
6	14.56	14.56

In the above graph we see that there is a trend now where the data is moving in the upward direction.

So, with that we come to the end of the discussion on the Data smoothing method. Hopefully it helped you understand the topic, for more information you can also watch the video tutorial attached down this blog. The blog is designed and prepared by Niharika Rai, Analytics Consultant, DexLab Analytics DexLab Analytics offers machine learning courses in Gurgaon. To keep on learning more, follow DexLab Analytics blog.

Probability PART-II: A Guide To Probability Theorems

Posted on October 19, 2020October 19, 2020 by Dexlab

This is the second part of the probability series, in the first segment we discussed the basic concepts of probability. In this second part we will delve deeper into the topic and discuss the theorems of probability. Let’s find out what these theorems are.

Addition Theorem

- If A and B are two events and they are not necessarily mutually exclusive then the probability of occurrence of at least one of the two events A and B i.e. P(AUB) is given by

Removing the intersections will give the probability of A or B or both.

Example:- From a deck of cards 1 card is drawn, what is the probability the card is king or heart or both?

Total cards 52

P(KingUHeart)= P(King)+P(Heart) ─ P(King∩Heart)

If A and B are two mutually exclusive events then the probability that either A or B will occur is the sum of individual probabilities of the events A and B.

P(A)+P(B), here the combined probability of the two will either give P(A) or P(B)

If A and B are two non mutually exclusive events then the probability of occurrence of event A is given by

Where B’ is 1-P(B), that means probability of A is calculated as P(A)=1-P(B)

Multiplication Law

The law of multiplication is used to find the joint probability or the intersection i.e. the probability of two events occurring together at the same point of time.

In the above graph we see that when the bill is paid at the same time tip is also paid and the interaction of the two can be seen in the graph.

Joint probability table

A joint probability table displays the intersection (joint) probabilities along with the marginal probabilities of a given problem where the marginal probability is computed by dividing some subtotal by the whole.

Example:- Given the following joint probability table find out the probability that the employee is female or a professional worker.

Watch this video down below that further explains the theorems.

At the end of this blog, you must have grasped the basics of the theorems discussed here. Keep on tracking the Dexlab Analytics blog where you will find more discussions on topics related to Data Science training.

Probability PART-I: Introducing The Concept Of Probability

Posted on October 12, 2020October 12, 2020 by Dexlab

Today we will begin discussion about a significant concept, probability, which measures the likelihood of the occurrence of an event. This is the first part of the series, where you would be introduced to the core concept. So, let’s begin.

What is probability?

It is a measure of quantifying the likelihood that an event will occur and it is written as P(x).

Key concepts of probability

A union comprises of only unique values.

Intersection comprises of common values of the two sets

Mutually Exclusive Events:- If the occurrence of one event preludes the occurrence of the other event(s), then it is called mutually exclusive event.

P(A∩B) = 0

Independent Events:- If the occurrence or non-occurrence of an event does not have any effect on the occurrence or non-occurrence of other event(s), then it is called an independent event. For example drinking tea is independent of going for shopping.
Collectively Exhaustive Events:– A set of collectively exhaustive events comprises of all possible elementary events for an experiment. Therefore, all sample spaces are collectively exhaustive sets.
Complementary Events:– A complement of event A will be A` i.e. P(A`) = 1 ─ P(A)

Properties of probability

Probabilities are non-negative values ranging between 0 & 1.
Ω = 1 i.e. combined probability of sample is 1
If A & B are two mutually exclusive events then P(A U B)= P(A) +P(B)
Probability of not happening of an event is P(A)= 1 ─ P(A)

Rules of Counting the possibilities

The mn counting rule:- When a customer has a set of combinations to choose from like two different engines, five different paint colors and three different interior packages , how will he calculate the total number of options available to him? The answer to the question is “ mn counting rule”. Simply multiply the given options, like in our case 2 * 5 * 3 will give us 30.This means the customer has 30 combinations to choose from when it comes to purchasing a car.
Sampling from a population with replacement:- Suppose that you roll a dice three times i.e. the number of trials is 3, now if we want to check how many combinations are possible in this particular experiment we use Nⁿ= 6³ = 216
Sampling from a population without replacement:- When the sample space shrinks after each trial then you use the following formula :-

Conclusion

There is a video covering the same concept attached down the blog, go through it to be more clear about this.

So, with this we wrap up our discussion on the concept of probability. If you want more informative blogs on Data Science training, then follow the Dexlab Analytics blog. Dexlab Analytics provides machine learning certification courses in gurgaon as well.

What Is The Role Of Big Data In The Pharmaceutical Industry?

Posted on October 8, 2020October 8, 2020 by Dexlab

Big data is currently trending in almost all sectors as now the awareness of the hidden potential of data is on the rise. The pharmaceutical industry is a warehouse of valuable data that is constantly piling up for years and which if processed could unlock information that holds the key to the next level of innovation and help the industry save a significant amount of money in the process as well. Be it making the clinical trial process more efficient or, ensuring the safety of the patients, big data holds the clue to every issue bothering the industry. The industry has a big need for professionals who have Data science using Python training, because only they can handle the massive amount of data and channelize the information to steer the industry in the right direction.

We are here taking a look at different ways data is influencing the pharmaceutical industry.

Efficient clinical-trial procedure

Clinical trial holds so much importance as the effectiveness of a drug or, a procedure on a select group of patients is tested. The process involves many stages of testing and it could be time-consuming and not to mention the high level of risk factors involved in the process. The trials often go through delays that result in money loss and there is risk involved too as side effects of a specific drug or a component can be life-threatening. However, big data can help in so many ways here, to begin with, it could help filtering patients by analyzing several factors like genetics and select the ones who are eligible for the trials. Furthermore, the patients who are participating in clinical trials could also be monitored in real-time. Even the possible side effects could also be predicted and in turn, would save lives.

Successful sales and marketing efforts

The pharmaceutical industry can see a great difference in marketing efforts if only they use data-driven insight. Analyzing the data the companies could identify the locations and physicians ideal for the promotion of their new drug. They can also identify the needs of the patients and could target their sales representative teams towards that location. This would take the guesswork out of the process and increase the chance of getting a higher ROI. The data can also help them predict market trends as well as understand customer behavior. Another factor to consider here is monitoring the market response to a particular drug and also its performance, as this would help fine-tune marketing strategies.

Collaborative efforts

With the help of data, there could be better collaboration among the different segments that directly impact the industry. The companies could suggest different drugs that could be patient-specific and the physicians could use real-time patient data to decide whether the suggestions should be implemented in the treatment plan. There could be internal and external collaborations as well to improve the overall industry functioning. Be it reaching out to researchers or, CROs, establishing a strong link can help the industry move further.

Predictive analysis

A new drug might be effective in handling a particular health issue and could revolutionize the treatment procedure but, the presence of certain compounds might prove to be fatal for certain patients and drug toxicity if not detected at an early stage could endanger a particular patient. So, using predictive analysis a patient data could be analyzed to determine the genetic factors, disease history, as well as lifestyle. The smart algorithms thereby help identify the risk factors and makes it possible to take a personalized approach regarding medication that could prove to be more effective rather than some random medication.

Big data can increase the efficiency of the pharmaceutical industry in more ways than one, but compared to other industries somehow this industry still hasn’t been able to utilize the full potential of big data, due to factors like privacy and, monetary issues. The lack of trained professionals could also prove to be a big obstacle. Sending their select professionals for Data Science training, could prove to be a big boon for them in the future.

Bringing Back Science into “Data Science”

Posted on September 25, 2017May 23, 2020 by Dexlab

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!

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