Data science has been disrupting the industries for the past couple of years. With the advent of technology, data and the hidden insights in them are widely being used to improve every industry. The industries like finance and health care have already made their way up using data science technology. This article discusses whether data scientists are important to pharmaceutical companies.
Well, data in the hands of a skillful data scientist have great value. The data in Pharmaceutical companies are no different. With the aid of a data scientist, these companies hold great opportunities to improve their operations. How exactly do they help? Keep reading to find out.
The Drug Development
Unlike many challenging problems resolved by engineers, drug development has no overall model serving as a basis for optimization. This multidisciplinary and complicated process is in great due for a significant improvement. Pharmaceutical companies nowadays are generating great amounts of data, driven mostly by the sequencing of human genomes. These companies are now realizing the importance of data scientists in developing algorithms that can uncover efficiencies by analyzing this data. Since the time of its inception, pharmaceutical companies are striving to meet the new clinical needs without affecting their financial health. The data science approach is expected to play a critical role in meeting this requirement.
Clinical Trial Planning
The phase III clinical trials have always been a headache for the pharmaceutical companies, especially in the light that the patent exclusivity of a drug starts roughly around the time of its first clinical trial. The practical complications with recruiting and randomizing a sufficient number of patients result in increased costs and delays. It also erodes the time over which the company can recoup the costs during the period of patent exclusivity. Data-driven approaches are now taking over this issue. They are now resorting to data science to build precision into the way they calculate the feasibility of successful clinical trials within the time and expense constraints.
Drug Repurposing
Drug repurposing is an attempt to find an altogether different use for a drug. It can be attempted on both drugs that are on the market and the ones stopped developing. It is usually done by evaluating a hypothesis put together by a scientist in a laboratory. It eliminates the time and cost incorporated with developing a medicine. With the recent update brought by data science, this discovery can be made quicker by computation of complementary “drug-disease” pair on large public repositories of sequencing and gene expression data. It will ultimately result in cheaper medicines.
Wearables
Wearables technology provides a method of unobtrusively capturing continuous physical measurements. They are aimed at replacing the expensive instruments that are traditionally used. With the aid of data science, pharmaceutical companies are aiming to solve the problem in medication adhering in clinical trials. So, it is clear that pharmaceutical companies are also in need of trained data scientists. You can start your journey to a successful data science career by taking the data science prodegree by Imarticus Learning. It provides with all the necessary skills required for your career. It is one of the best data science course in Mumbai.
Tag: Data Science
What Are the Most Common Questions Asked in Data Science and Machine Learning Interviews?
Data Science and Machine Learning have grown leaps and bounds in the last couple of years. Data science is essentially an interdisciplinary field that focuses on extracting data in different structured or unstructured forms by using various methods, algorithms and processes. Machine learning, on the other hand, is the ability to learn with data. It uses a mixture of artificial intelligence and statistical computer science techniques which help interpret data efficiently, without having to use explicit and large programs.
As more people look into these fields as prospective career choices, the competition to get recruited by companies in either of these fields is quite strong.
Thus, here is a list of a few frequently asked questions related to Data Science and Machine learning that you can expect in your interview.
1) Explain what data normalization is, and its importance.
This is one of the basic, yet relevant questions that are usually asked. Data normalization is a pre-processing step. It helps weight all the features that fit in a particular range equally. This prevents any kind of discrepancy when it comes to the cost function of features.
2) Highlight the significance of residual networks.
Residual networks and their connections are mainly used to facilitate easier propagation through any given network. Thus, residual connections allow you to access certain features present in the previous layers directly. The presence of residual networks helps make the network as more of a multi-path structure. This gives room for features to tread across multiple paths, thus helping with better propagation throughout the system as a whole.
3) Why are convolutions preferred over FC layers for images?
Though this is technically not a very common question, it is interesting because it tests your skills related to comparison and problem-solving. FC layers have one major disadvantage which is that they have no relative spatial information. On the other hand, convolutions not only use spatial information but also preserves and encodes it. Also, Convolutional Neural Networks (CNN) are said to have a built-in variance which makes each kernel a feature detector on its own.
4) What do you do if you find missing or corrupted data in any dataset?There are mainly two things that you can do if you find missing or corrupted data in a dataset.
- Drop the respective rows or columns: This can be done by using two method functions, isnull() or dropna(). This will help you determine if any dataset is actually empty. If it is empty, you can simply drop it.
- Replace the data with non-corrupted values: To replace any invalid value with another value, the fillna() method can be used.
5) Why are 3×3 convolutional kernels preferred over larger kernels?
Smaller kernels such as a 3×3 kernel generally use lesser computations as well as parameters. Thus, you can use several smaller kernels as opposed to a few larger ones. Also, larger kernels do not capture as much spatial content as smaller kernels do. Apart from this, smaller kernels use a lot more filters than larger kernels do. This, in turn, facilitates the use of more activation functions which can be used for discriminative mapping functions.
6) Why does the segmentation of CNN have an encoder-decoder structure?
The segmentation structure of CNN’s is usually in the encoder-decoder style so that the encoder can extract features from the network while the decoder can decode these features to predict the segments of the image under consideration.
Thus, looking into simple questions like this that focus on your knowledge of the concepts of Data Science and Machine Learning will really help you face an interview while applying for a position in the field.
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How Should You Prepare For Statistic Questions for Data Science Interviews
Data Science has been the buzz word of the IT field for the past few years. Courses like data science course from Imarticus will equip you with all the skills required for a data science job. However, to ace the interviews for data science jobs, you should be well versed with the basic components of statistics too. This article discusses one of the key element in Data Science, statistics and its relevant topics to brush up before a data science job interview.
Preparing for Data science interviews
As in many interviews, the statistics are also going to start with technical questions. Many interviewers try to test your knowledge and communication skills by pretending to have no idea about the basic concepts and asking you to explain them. So, it is important to learn how to convey complex concepts without using the assumed knowledge.
Following are the few important topics you could brush off before attending the interview.
1. Statistical features
They are probably the most used statistics concept in data science. When you are exploring a dataset, the first technique you apply will be this. It includes the following features.
- Bias
- Variance
- Mean
- Median
- Percentile and many others.
These features provide a quick, informative view of the data and are important to be familiar with.
2. Probability Distribution
A probability distribution is a function that represents the probabilities of occurrence of all possible values in the experiment. Data science use statistical inferences to predict trends from the data, and statistical inferences use probability distribution of data. So it is important to have proper knowledge of probability functions to work effectively on the data science problems. The important probability distributions in the data science perspective are the following.
- Uniform Distribution
- Normal Distribution
- Poisson Distribution
3. Dimensionality Reduction
It is the process of reducing the number of random variables under consideration by taking a set of principle variables. In Data Science, it is used to reduce the feature variables. It can result in huge savings on computer power.
The most commonly used statistical technique for dimensionality reduction is PCA or Principal component analysis.
4. Over and Under-Sampling
Over and Under Sampling are techniques used to solve the classification problems. It comes handy when one dataset is too large or small relative to the next. In real life data science problems, there will be large differences in the rarity of different classes of data. In such cases, it is this technique comes to your rescue.
5. Bayesian Statistics
Bayesian statistics is a special approach to applying probability to the statistical problems. It interprets probability as the confidence of an individual about the occurrence of some event to happen. Bayesian statistics take evidence to account.
These topics from statistics are very important for a Data Science job and make sure you learn more about them before your interview. You can also try various data science training in Mumbai to begin your career at right note. Genpact data science course from Imarticus is an excellent choice to learn more about data science. Check out and join the course immediately.
How is MySQL Used In Data Science
Data Science is considered to be the most sought-after profession of the 21st century. With lucrative opportunities and large pay scales, this profession has been attracting IT professionals around the world. Various tools and techniques are used in Data science to handle data. This article talks about MySQL and how it is used in data science.
What is MySQL
In short words, MySQL is a Relational Database Management System or RDBMS that use Structured Query Language (SQL) to do so. MySQL is used for many applications, especially in web servers. Websites with pages that access data from databases use MySQL. These pages are known as “Dynamic Pages” since their contents are generated from the database as the page loads.
Using MySQL for Data Science
Data science requires data to be stored in an easily accessible and analyzable way. Even though there are various methods to store data, databases are considered to be the most convenient method for data science.
A database is a structured collection of data. It can contain anything from a simple shopping list to a huge chunk of data of a multinational corporation. In order to add, access and process the data stored in a database, we need a database management system. As mentioned MySQL is an open-source relational database management system with easier operations enabling us to carry out data analysis on a database.
We can use MySQL for collecting, Cleaning and visualizing the data. We will discuss how it is done.
1. Collecting the Data
The first part of any data science analysis is collecting the massive amount of data of data. The Sheer volume of data often causes some insights to be lost or overlooked. So, it is important to aggregate data from various sources to facilitate fruitful analysis. MySQL is capable of importing data to the database from various sources such as CSV, XLS, XML and many more. LOAD DATA INFILE and INTO TABLE are the statements mostly used for this purpose.
2. Clean the Tables
Once the data is loaded to the MySQL database, the cleaning process or correcting the inaccurate datasets can be done. Also deleting the dirty data is also part of this step. The dirty data are the incomplete or irrelevant parts of the data.
The following SQL functions can be used to clean the data.
- LIKE() – the simple pattern matching
- TRIM() – Removing the leading and trailing spaces.
- REPLACE() – To replace the specified string.
- CASE WHEN field is empty THEN xxx ELSE field END – To evaluate conditions and return value when the first one is met.
3. Analyze and visualize data
After the cleaning process, it is time to analyze and visualize the meaningful insights from the data. Using the standard SQL queries, you can find relevant answers to the specific questions.
Some analysis examples are given below:
- Using query with a DESC function, you can limit the results only to the top values.
- Display details of sales according to the country, gender or product.
- Calculate rates, evolution, growth and retention.
If you would like to know more about MySQL and its use in Data Science join the data science course offered by the Imarticus. This Genpact data science course offers a great opening to the career opportunities in Data Science. Check out the course and join right away.
What is the difference between data science and data analytics?
One of the biggest jobs in the technological center is working with big data. There are plenty of roles within this sector and two of the most popular ones include data science and analytics. While a lot of companies tend to hire similar candidates for these roles, there is still a difference between the two.
It is important that you understand the two roles before you choose a career path in either. If you’re looking to kickstart a career in the field of big data, then knowing the difference between data science and analytics is a good pointer to keep in mind.
What is data science?
Data science is a broader term for different methods and models used to get information. Under data science are the statistics, scientific methods, and math along with other tools which can be used to manipulate and analyze data. If there is a process or a tool that can be used on data to analyze it and extract information from the same, then it falls under the umbrella of data science.
As a practitioner of data science, you’ll have to connect data points and information to figure out connections which can be more useful for a business. It requires you to explore the unknown and find newer patterns or insights which can then be turned into actionable decisions from a business perspective. Data science attempts to delve into connections and figure out methodologies which work for the betterment of a business.
What is data analytics?
Data analytics is more concentrated and specific than data science. It is focused on achieving a specific goal by sorting through large data sets and looking for ways to support the same. Analytics are more automated as they can help in providing better insights in certain areas. Data analysis also involves analyzing large data sets to find smaller, more useful pieces of information to fulfill an organization’s goals.
Analytics basically sorts data into things that an organization knows or doesn’t know and can be used to measure any event in the present, past or even future. It moves from insights to impact and connects patterns and trends along with the true goals of a company, keeping the business aspect in mind.
Knowing the difference:
Data analysts and scientists perform different roles and companies must know exactly what they’re looking for. Data analytics usage in industries such as travel, gaming or healthcare, where analysts can extract specific data to improve business, data science is used in more broader categories such as digital advertising or internet searches.
Data science also plays a role in developing machine learning and Artificial Intelligence. Companies are looking at systems which allow computers to go through large amounts of data. They then formulate algorithms, developed by analysts which can sift through the same and find connections which can help them reach their objectives and thus, bring in more revenue.
Imarticus provides the best data analytics course to make it easier for anybody looking to enter the world of big data science and kickstart their career.
For more details regarding this, you can directly visit – Imarticus Learning and can drop your query by filling up a simple form through the site or can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Banglore, Hyderabad, Delhi, Gurgaon, and Ahmedabad.Is Data Analytics An Interesting Career Field?
One of the biggest job sectors of the last few years, data analytics is seen as one of the most lucrative career options today. In the United States, an estimated 2.7 million jobs are predicted to be taken by data science and analytics by 2020. The value that big data analytics can bring companies is being noticed and companies are looking for talented individuals who can unearth patterns, spot opportunities and create valuable insights.
If you’re someone who’s good at coding and looking to make the next jump from a career perspective, then data science could be your calling. Here are a few reasons you should look out for a career in data analytics:
Higher Demand, Less Skill:
India has the highest concentration of data scientists globally, and there is a shortage of skilled data scientists. According to a McKinsey study, the United States will have 190,000 data scientist jobs vacant due to a lack of talent, by 2019. This opens the door for a good data analyst not just to make money, but own the space.
Good data analysts can take complete control of their work without having to worry about interference. As long as you can provide crucial insights which contribute to the company’s business, you’ll find yourself moving up the ladder faster than expected.
Top Priority in Big Companies:
Big data analytics is seen as a top priority in a lot of companies, with a study showing that at least 60% of businesses depend on it to boost their social media marketing ability. Companies vouch by Apache Hadoop and its framework capabilities to provide them data which can be used to improve business.
Analytics is being seen as a massive factor in shaping a lot of decisions taken by companies, with at least 49% believing that it can aid in better decision making. Others feel that apart from key decisions, big data analytics can enable Key Strategic Initiatives among other benefits.
Big Data Is Used Almost Everywhere:
Another great reason to opt for big data or data analytics as a career option is because they are used pretty much everywhere! With the highest adopters of the technology being banking, other sectors which depend on big data include technology, manufacturing, consumer, energy and healthcare among others.
This makes big data an almost bulletproof option because of the wide range of applications it can be used for.
Most Disruptive Technology In The Next Few Years:
Data analytics is also considered as one of the most disruptive technologies to influence the market in the next few years. According to the IDC, the big data analytics sector is touted to grow to up to $200 billion by 2024.
Thus, big data analytics is going to be the future of computing and technology. The sector is seeing massive growth and a lot of demand. The more you’re able to provide insights that can make a difference in this sector, the higher are your chances of getting a lucrative job.
Whether it’s a data analytics course in Bangalore or any other city, Imarticus will be able to provide you with the right kind of training and knowledge with data analytics courses to help your career soar.
7 Awesome Lessons You Can Learn From Studying Data Science
Data archives have increased exponentially, and it’s posing great challenges to various industries. Fortunately, they have gone beyond ‘What is Data Science?’ to finally adopt Data Science analysis, to derive something meaningful and productive from the existing pile of information.
Today there are many takers for data science training, and people have learnt some important lessons. Let’s discuss some of them:
It’s important to understand business as a whole
At times people give too much emphasis on technical knowledge and out the domain knowledge on the backburner. This way they end up creating a sophisticated model without really understanding the business needs. Such models don’t add much value to the business, regardless of their accuracy.
As a data scientist, one needs to understand a business through the eyes of data. Only having the technical knowledge won’t help you articulate your ideas to colleagues in the context of business. So, besides Jargons, it’s important to learn the commonly used terms pertaining to a business.
It’s important to have a penchant for details
Data scientists can’t carry out data cleansing and transformation without having an eye for details. Data in real-world scenarios is never arranged perfectly, and one needs to isolate a lot of noise from it, to arrive at something meaningful. So, a detail-oriented mindset is a must to succeed in Data Science. Without that, you may not derive insightful results from your Exploratory Data Analysis. You may put your heart and soul into the data cleaning process, but still the data might not be reliable enough to be used by your Model.
Framing logic and designing an experiment
Machine learning problems are not that complicated, as you just need some data for training purpose in order to build your model. In case of Data science, there is a well-structured workflow that provides a larger picture of the undergoing processes (Data cleaning to Model interpretations). There is a component called Experiment which is a part of the workflow. It includes the logic for hypothesis testing and Model building.
Therefore, data science helps in framing a logic and designing an experiment for real-world scenarios, to test certain assumptions and evaluate the Model. You can understand more about this aspect by opting for a Data Science course.
Communication skills
If you are a Data Scientist, you better enhance your communication skills, as it will help you sail through. As mentioned earlier in the write-up, there is no point in acquiring the technical knowledge and crunching the data all day long, if you can’t communicate your ideas to stakeholders in a business-friendly language. This affects your credibility as well as your professional relationships. In short, it’s a lose-lose situation.
As a data scientist, your biggest challenge is to put forward your most complex ideas and insights in a layman’s language, so that even a 15 years old can understand them. Your language should make your colleagues feel empowered, so that they can invest emotionally and intellectually.
Art of Storytelling
If you think that Data Science is all about crunching data and building models, then you are mistaken. It’s also about weaving a compelling story based on data analysis, to indulge the stakeholders. Depending on the project goals, the story should cover the following questions:
> What’s the reason to analyze the data?
> What insights can be obtained from the results?
> Can any action plans be derived out of the analysis?
Often the art of storytelling is ignored over data-driven analysis. Lousy storytelling or boring presentations, can greatly undermine the valuable results from even some of the best models.
It’s important to set a benchmark for comparison
It’s naïve to assess the efficiency of a Model without comparing it with other models. Without a benchmark, it always difficult to define ‘What is good’, and the results can’t be fully trusted.
Art of Risk management
Every Model is built, keeping in mind the best and the worst-case scenarios. You are required to explain your model’s limitations to stakeholders, and how much risk can the company potentially bear if the model goes to production. This is where Risk Management comes into picture. If you understand what’s at stake and have a plan to minimize the risk involved, then only you can take stakeholders into confidence. To understand more about the art of risk management, you may opt for a Data science course.
Intensive Data Science training can help you realize the above-mentioned lessons. If you are an aspiring Data Scientist, we hope this write-up served the purpose.
Build Your Own AI Applications in a Neural Network
Today Big Data, Deep Learning, and Data Analytics are widely applied to build neural networks in almost all data-intensive industries. Machine learning courses in India offers such learning as short-term courses, MOOCs, online classrooms, regular classrooms, and even one-on-one courses. Choices are aplenty with materials, tutorials and options for training being readily available thanks to high-speed data and visualization made possible by the internet.
The study on jobs in Data Sciences says that core skills in Python are preferred by recruiters and is requisite for jobs in data analytics. The challenge lies in formulating a plan to study Python and the need of a specialist to help understand the technical and practical aspects of this cutting edge technology.
Why do a Specialization Course for Beginners?
Not all are blessed with being able to learn, update knowledge and be practically adept with the Python platform. It requires a comprehensive knowledge of machine learning, understanding of data handling, visualization techniques, AI deep learning, statistical modelling and being able to use your expertise on real-time practical examples of data sets from various industries.
Machine learning courses and case studies on Python platform are conducted in flexible learn-at-your-own-pace sessions in modes like instructor-led classroom sessions at select locations, virtual online classes led by certified trainers or even video sessions with mentoring at pre-determined convenient times.
One can do separate modules or certificate Big data Hadoop training courses with Python to understand data science analytics and then opt for modules using AI for deep learning with Python or opt for a dual specialization by doing the beginners course and courses covering AI and Deep Learning with Python. The areas of Deep Learning and AI both require prior knowledge of Deep Learning, Machine Learning, and data analytics with Python.
An example of one such course is the AnalytixLabs starter classes in Gurugram and Bangalore as a speedy boot-camp followed by a package of two courses in AI Deep Learning with Python and the Data Science with Python. The prerequisites are knowledge of at least one OOPs language and familiarity with Python. Their 36 classes, 250-hour course offers dual specialisations, and 110 hours of live training using multiple libraries in Python.
Just ensure you choose the right course to allow your career prospects to advance and allows further learning in Python-associated specialised subjects.
Why is Data Science So Famous?
Data Science is clearly the way to the future and is revolutionizing a number of fields across industries. In just a few years, it has emerged as the most sought-after career route. In spite of all the hype, a lot of people end up asking ‘What is data science, exactly?’
Data analytics is basically the examination of data, and a data science course mainly equips young tech enthusiasts to sift through huge amounts of scrambled data to process them and extract information out of them.
From healthcare to politics to disaster management, data science is making way for breakthroughs all over. Celebrated computer scientist Jim Gray considered data science to be a fourth paradigm of science, and insisted that information technology is changing everything about science.
Decades after his prophecy, he stands corrected, and a career in data science is one of the most lucrative career aspirations you can have. But it is very important to know exactly what data analytics does, and how it is changing the world around us.
So, what is data science and why exactly is it the hottest career right now?
Did you know that according to the company review website Glassdoor, data science was the highest paid field in 2016? Glassdoor is basically a platform where employees can rate their workplace and its management. The 2016 report was actually based on reviews of the people in the data science field, and their income growth. The survey also took into account the possibilities for career growth of the people working in the field.
You must understand that a data science course consists of a number of skills which the aspirants must marvel at, like programming, statistics, coding etc, and this makes their skill set a very coveted one in the field of analytics. Data science training is still mainly about about figuring out trends and patterns based on statistics and jumbled data. More and more companies are hiring data scientists for strengthening their analytics team, which is why the data science field is such a lucrative one.
In the field of artificial intelligence (AI), especially, data science training is an invaluable asset. You must have heard about the exponentially growing influence of AI in today’s industries. Every major company is seeking data scientists who specialize in AI. To put it simply, a successful AI assignment is not possible without the right data, which is extracted by a data scientist and processed to their advantage.
Let’s look at other simple examples. Take for instance, companies like Google. Their operations and service depends almost entirely on successful data analysis. Are you aware that the HR department at Google has completely changed the game for other corporate companies, when it comes to perfecting work culture?
They have moved to a form of data-based employee management, where they sift through data and process it to make the company a better place to work for their employees. As a result, research has shown that Google is the best corporate company to work in the world right now.
Some of the best and the most successful companies in the world are investing millions of dollars to amp up their data science branch, and this hardly comes as a surprise in the era of information technology. Even small businesses need to study data and the statistics of the market before they can launch their products.
Not to mention a data scientist earns substantially better than his counterparts in other sectors, and it can only get better if the pattern is followed. Research has shown that the median salary of the data scientist is something around $110,000. In a couple of years, a data scientist with only a few years of experience will not have to look around for long to get better opportunities, considering the boom in the field and the overriding necessity of data analytics.
How to Build a Career in Data Science?
In order to start off on your journey of building a career in the field of data science, it is important for you to thoroughly analyse both the field and yourself in order to ensure the compatibility of the two. Generally speaking, any Data science professional, or Data Scientist as they are commonly known is supposed to have a certain skill set.
This skill set must include various skills and techniques like business expertise, data maneuverability, knowledge of SQL and other programming languages, a curious outlook towards their job, knowledge about the nuances of IT, receptiveness when it comes to teamwork, and most importantly the statistical expertise and ability to access to data.
Also Read Data Science – Things You Should Know
Thus, this skill set provides a candidate with the perfect amount of capabilities and opportunities to build a rewarding career in data science for themselves. Customization is the rule of the day in today’s economic setup, with every single firm out there trying their best to provide accurate and astute services to their target audiences. With the access to social media and the internet increasing manifold, simultaneously increasing is the need of professionals who are required to go through tons of information in order to find out the exact insights which would be needed to help the company achieve massive profits.
This is probably why many job searching sites reflect an increasing number of position offerings for professionals who are interested in the field of Data Science. So if you happen to be one such professional, then there are a lot of options for you to try out for in this field. These positions could range anything from a Business Intelligence Analyst, Data Mining Engineer, and Data Architect and of course the Data Scientist and many others. All of these positions do require an individual to be able to demonstrate all of the skills mentioned above.
With more and more big named companies like Oracle, Apple, Microsoft and Walmart demanding professionals working in the data science industry increases, at the same time there is also a great demand in general for such professionals. If we are to go by what a number of surveys are saying, by this year there are estimated to be more than 20,000 openings for data science professionals all over the world.
In order to grow your career in Data Science, it is necessary for you to have certain educational requirements. These don’t really refer only to the fact that you need to belong to a certain educational background, but they actually refer to the amount of knowledge that you have when it comes to certain data programming or data analytical tools.
These tools include R Programming, SAS Programming, Hadoop, SQL, Python, Hive and so on. Today there are so many institutes out there, both internationally and nationally that offer short-term training courses, in order to train professionals in the usage of such tools.In India especially there Imarticus Learning which is one such institute that offers industry endorsed courses in Data Science and Analytics.
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