Data Science

What are the different programming languages used in Data Science?

The following are the programming languages used in Data Science:

1. R
2. MATLAB
3. Python
4. SQL
5. SAS
6. Java
7. C#
8. C
9. Perl
10. Weka
11. Scikit
12. Octave
13. NumPy
14. JMP
15. Haskell
16. Excel

With mostly R, MATLAB and Python being used.

List the type of business analytics in data science.

The types of business analytics in data science are as follows:

1. Descriptive data analytics: It is the type which allows us to convert big data into smaller pieces of information. It is the simplest form/ class of data analytics. It given the answer to “What has happened?” It uses data aggression and data mining tools.
2. Predictive analytics: It utilizes various techniques related to statistical, modeling, machine learning and mining algorithms to reach predictive state or to predict what might happen in the future. It given the answer to “What could happen?” It uses statistical models and forecast settings to do that.
3. Prescriptive analytics: It is a type of predictive analytics. When we need to prescribe an action, the analyst makes use of prescriptive analytics. It predicts the possible consequences based on the different choice of action. It gives the answer to “What should we do?”

Explain the basic tools to carry out Exploratory Data Analysis (EDA).

The following description shows the list of tools used in carrying out EDA:

1. Excel: For inspecting, summarizing data minutely.
2. Trifecta: As Excel tools pose limitations in few usages, this tool is rapidly gaining popularity and usage.
3. Rapidminer: This is mainly used in building the machine learning models
4. Rattle GUI: It’s GUI is built on R which supports various algorithms related to Data Science.
5. Qlikview: This tool is mainly used in looking for various business insights, driving them and then presenting them in a perfect plus professional manner.
6. Weka: It is a machine-learning tool which is perfect for data preprocessing, regression and its classification.
7. KNIME: It is an open-source tool used in analyzing data which can later be deployed as and when required.
8. Orange: This is interactive in nature and mainly designed for performing tasks related to visualization and mining of data.

Analyze the basic selection feature algorithms (Filters, Wrappers, Decision Trees and Random Forests) in detail.

1. Filter method: It used external learning algorithm in order to analyze the selected features and their performance. Selection of features is mainly done on the basis of statistical properties.
2. Wrappers: It uses search algorithms for searching through the features and evaluate them by running predictive modeling.
3. Random forests: They are highly accurate, robust and offer great ease in usage. They consist mainly of decision trees. The methods they provide are mean decrease impurity and mean decrease accuracy.
4. Decision trees: Decision trees based algorithms are considered as the best and supervised learning algorithms. They give high accuracy, stability and ease of interpretation to tree- based methods. They map linearly as well as non-linear relationships very well. They solve all kinds of problems very well. It is mandatory for every analyst to learn these algorithms and use them for modeling.

Describe the basic machine learning algorithms for predictive modeling.

The basic machine learning algorithms used for predictive modeling are:

1. Naïve Bayes Classifier Algorithm: used in classifying the web-pages, documents, emails etc.
2. K- Means Clustering Algorithm: It is an unsupervised machine learning algorithm. T is mainly used for cluster analysis. This method is a non-deterministic and iterative in nature.
3. Support Vector Machine Algorithm: It is a supervised machine learning algorithm. It is mainly used for classification or regression problems. The data set teaches SVM about the classes so that SVM can classify any new data.
4. Apriori Algorithm: It is an unsupervised machine learning algorithm. It generates association rules from a given data set.
5. Linear Regression: Describes the relationship between 2 variables and how the change in one variable changes another one.

What are the tools used by Data Scientists?

The most used tools by Data Scientists include R which is used most extensively by Data Scientists. It is used for most mathematical operations and then seeing the visualized form of results. There are additional packages like Parallel, Snow, Rhadoop and Rhipe which help R in managing big datasets and parallel processing techniques.

JAVA and JAVA virtual machine support data mining by means of their open-source libraries like Mahout and Meka. Other languages which can be used along with JVM are Scala, Clojure etc.
Python is another high-level language which is used in data science as it has rich libraries which support data operations.

Excel is also a very popular tool on a smaller scale. SAS(Statistical Analysis system) is another data mining software suite used for advanced analytics, managing the data and social media analytics.

MySQL, an open-source RDBMS, MONGODB, Oracle are also used by data scientists as powerful DBMS. Hadoop is used for file system computing. A/B testing for removing unnecessary errors.

What are the job titles and designations for “Data Scientists”?

The following description talks about the job roles and designations of Data Scientists. They are:

1. Data Scientists: They are the people who focus on applying the theoretical knowledge about statistics and algorithms in order to find the best solution of the problem present in front of them. They apply from the minute, basic to the most advanced concepts of statistics and algorithms to come to a great conclusion. They work in the midst of programming and implementing Data science techniques and concepts.
2. Statisticians: They work in implementing the statistical approaches on data.
3. Data managers: Their job is to run or manage the teams that are doing the data science work.
4. Data engineers: They make use of IT and CS concepts to implement data science models and handle them.
5. Data architects: They build up technical structures to manage data models.
6. Data Administrators: They manage data models and their related solutions.
7. Data Analysts: To extract details from graphs and charts. For example, analyzing charts or bar graphs to come to a specific conclusion.

What are the salary trends in the market for “Data Scientists” across countries?

What are the future prospects for “Data Scientist”?

Advancement in technology: A new tech is always introduced after running several trials based on the analysis of the data is present related to that tech.

1. Maintenance: By doing the prediction analysis using the data regarding electronic goods, it will help in predicting when we need to replace an electronic part or maintenance of it.
2. Medicinal Safeguarding: by data analysis of life cycle of different salts, the expiry date of the drugs is being provided.
3. Speech recognition: This is related to artificial intelligence. We make machine learning also to make the computer understand what we speak. It is still under construction for providing 100% accuracy. It is being improved by data analysis only, one of the jobs of data scientists.
4. Growth in Data: Every day the amount of data being produced all over the world is increasing rapidly, so to handle this messy data and to generate a useful information from it, we require data scientists to analyze.
5. All the advancements require data analysis and hence data scientist, e.g., Google is recently working on its latest technology ‘Google Tango’, a tech to create 3D maps of surroundings in the live environment, using our Android phones.

What are the different sectors/fields for “Data Scientists” to work?

Different sectors/ fields for “Data Scientists” to work are:

1. Biotech: In Genomics
2. Energy: From discovering new energy sources to planning for better efficiency and reducing implementation costs.
3. Finance: Analyzing new pieces of data and making predictive models, doing predictive analysis.
4. Gaming and Hospitality: From capturing website visits to video feeds to working on analyzing them and detecting any useful feature.
5. Government: To enhance security and to prevent any misuse of data. Also, to prevent soldiers and defense circuitry.
6. Health-care: working on data related to health tests, medicines, genomics, patients and their numbers, their health history etc.
7. Insurance

What are the Prerequisites of Data Science Course?

Having Masters /Ph.D./Graduate Degree in any of the STEM fields, basics in Programming, SQL basics, Good knowledge on basic math and statistic concepts.

What are the system requirements to attend the live sessions?

1. I3 Processor with 4GB RAM, OS can be 32 or 64 bit (Laptop/Desktop)
2. Internet connection with Min 1 MBPS speed
3. Good quality headset
4. Power back up
5. You can also log in through your Android mobile phone/ Tablet with 4G internet connectivity

What if the trainee misses any session?" connect="554"]Trainee can watch the recorded video of all the sessions in the LMS or Trainee can attend the missed session in the upcoming batches.

What do the trainee get from the LMS?

Trainee will have the access to Recorded sessions, Assignments, Quizzes, Case Studies, few course documents posted by trainers, Placement related docs etc.

What is the validity of the LMS access? What if the LMS access is expired.

Trainee will get 1-year access to the LMS. You can contact our support team to extend the validity of the LMS.

Will the trainee get any project to work on with Big data and Hadoop developer course?

Yes, Of course! The trainee will get the project at the end of the course, you need to submit a project. Our trainers will assist you to complete the project.

How are the practicals done?

Trainee will get step by step assistance from our expert trainers during the practical sessions, post live sessions, you can practice at your end and submit your queries if any to our support team support@bumacoglobal.com for further assistance.

What are the types of training we offer?

1. WBLT- Web-based live Training
2. WBVT- Web-based Video Training
3. One on One live training
4. Self-paced training
5. In class training

What are the benefits of online training?

1. Flexible location
2. Flexible schedule
3. Travel free
4. Time saving
5. Cost saving
6. LMS access
7. You will never miss a class
8. Two-way interactive
9. Fast learning
10. Trainer support for 1 year

Who are our Trainers?

Our trainers are industry experts having 10 to 15 years of industry experience and 3-4 years of training experience. Most of the trainers are working professionals who teach the real time scenarios which will help the students to learn the courses in an effective manner.

Will the trainee get the certification post the course completion?

Yes, Trainee will get the participation certificate from CorpConsult upon successfully completing the course.

What if the trainee has more queries and need assistance?

Trainee can drop an email to support@bumacoglobal.com an automatic ticket will get generated. Our support team works 24/7 to assist you with all your queries.