Big Data Analytics Using Python | Python Big Data Tutorial | Python And Big Data | Simplilearn

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This Big Data Analytics using Python tutorial will explain what is Data Science, roles and responsibilities of a Data scientist, various applications of Data Science, how Data Science and Big Data work together and how andwhy Data Science if gaining importance.
Every sector of business is being transformed by the modern deluge of data. This spells doom for some, and creates massive opportunity for others. Those who thrive in this environment will do so only by quickly converting data into meaningful business insights and competitive advantage. Business analysts and data scientists need to wield agile tools, instead of being enslaved by legacy information architectures.

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What are the course objectives of this Big Data and Data Science Course?

Mastering the field of data science begins with understanding and working with the core technology frameworks used for analyzing big data. You’ll learn the developmental and programming frameworks Hadoop and Spark used to process massive amounts of data in a distributed computing environment, and develop expertise in complex data science algorithms and their implementation using , the preferred language for statistical processing. The insights you will glean from the data are presented as consumable reports using data visualization platforms such as Tableau.

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Why should you take this Big Data and Data Science Course?

As an expert in this field, you will need to have a working knowledge of the three key pillars in the analytics ecosystem: data management, data science and reporting and visualization. This master’s program will hone your skills in:

Big Data:
Big data management is the ability to store and process voluminous amounts of unstructured data. Today with the overflow of online information, most companies are adopting big data practices to manage these huge volumes. Hadoop provides the distributed file system for storage, and MapReduce programming in Java is used for the processing. In the analytics lifecycle, it is critical to be able to store and query data to feed the necessary algorithms.

Data Science:
Data Science algorithms use data to create insights. Once you have an effective way to crunch data, you can use historical data for descriptive and predictive analytics. This is done using a programming language like R or Python, which utilize libraries for statistical analysis. Learning these languages are important to be able to design custom models for analytics, a key expectation for any data scientist. These skills range from basic probability to advanced machine learning.

Reporting and Visualization:
Once you have insights into data, it is important to make the insights available to the organization using visualization and reporting.

This program also includes a number of electives to ensure you get broad knowledge of the entire ecosystem and complementary skills in these fields. The two-year period ensures you have enough time to ramp up, develop skills and apply them in real world scenarios.

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Who can take this Big Data and Data Science Course?

Many roles can benefit from this program and pursue new career opportunities with high salaries, including:
1. Software developers and testers
2. Software architects
3. Analytics professionals
4. Business analysts
5. Data analysts
6. Data management professionals
7. Data warehouse professionals
8. Project managers
9. Mainframe professionals
10. Graduates aspiring to build a career in analytics
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