Integrated program in Data Science and Big data is a master's degree program designed to assist you in taking your initial steps into the real world of data science and big data. There is a high demand for experts who can convert data analysis into a relative benefit for institutions. This learning path will give you skills necessary for developing frameworks such as Spark, R, and Hadoop- to process large amounts of data and prosper in your big data profession.
Below is the list of courses it offers:
• Machine Learning
• Data science using Python
• R Programming- Data science certification Training
• Spark developer and Big Data Hadoop
• Tableau Desktop 10 Skilled Associate Training
Mastering the data science field starts with comprehending and operating with the fundamental technology frameworks used for big data analysis. You will learn the programming and developmental frameworks such as Spark and Hadoop used for processing vast amounts of data in a distributed coding environment, and design expertise in complicated data science algorithms and how to implement them using R, the most preferred programming language for statistical processing. The visions you will garner from the data are forwarded as valuable reports using platforms of data visualization, like Tableau.
You will get exposed to advanced machine learning skills after you have mastered data and information management and analytical methods.
Skills to learn:
This is a comprehensive course for data science and big data enthusiasts. It spans all key technologies in data science, big data, and reporting and visualization. The program is developed to enhance your potential at every step; thus, experts recommend you to follow the path as it ensures a charming conversion at the end of the mastering program. The path of learning is as follows:
1. Data science with R- this learning path will train you in the R Programming language and essential predictive and statistical logical concepts.
2. Spark developer and Big Data Hadoop- this training teaches you the several Hadoop ecosystem components like Hadoop 2.7, Pig, Yarn, Sqoop, MapReduce, Flume, as well as Apache Spark. It is also arranged for Cloudera CA175 accreditation.
3. Tableau desktop associate learning- this training will help you master the several concepts of building visualization, Tableau desktop, designing dashboards, and organizing data. It will prepare you for the Tableau Desktop skilled associate accreditation.
4. Data science using Python- this course introduces several packages in Python programming such as Pandas, SciPy, NumPy, and Scikit-learn for doing data analysis.
5. Machine learning- this training assists you in gaining a comprehension of Machine learning algorithms and applications. It also covers Spark Machine Learning and deep learning.
How is the program important?
As a data science expert, you will want to have a skillful knowledge of the three prominent pillars in the analytics environment: data science, reporting &visualization, and data management.Data science
Data science applications and algorithms use data for the creation of insights. You can utilize historical information for predictive and descriptive analytics once you gain a practical way of crunching data. This is performed using R or Python programming languages, which use libraries for arithmetical analysis, a prominent expectation for any data science expert.
Reporting & Visualization
Once you get data insights, it is good to make them available to the institution, utilizing visualization and reporting. The program also entails several electives that make sure you get an extensive knowledge of the whole environment and balancing expertise in these fields. The 2-year learning period makes sure that you have sufficient time to develop skills, ramp up, and apply them to real-world problems.
Management of big data is the capability to process and store vast amounts of unorganized data. Nowadays, with the overflow of online data, many organizations are implementing big data techniques to manage these large volumes. Hadoop offers the dispersed file system for data storage, and MapReduce coding using Java is used for information processing. In the lifecycle of analytics, it is important to be capable of storing and querying information to feed the necessary algorithms.
Gaining proficiency in big data and data science is critical in the analytics field in order to adjust to quick-changing spontaneous and trends business opportunities exposed by the available data. Some of the experts who can benefit directly are persons aspiring to become data/business analytics experts, software developers, mainframe professionals, architects, or data warehouse experts.