Courses to expand your knowledge on big data analysis
Big Data Analytics is a method of assessing big data and extracting information from it. In Today’s generation, when everyone has access to mobile phones and the internet, a lot of unorganized data is generated and collected from multiple sources.
Big data analytics is used to extract useful information and help businesses track consumer preferences, market trends, and patterns. All this data is collected by corporations and analyzed to yield better-formulated insights to help make informed decisions. A few examples of big data are when we click on an ad, use our emails, or search for something.
Big data has been in fashion for over a decade now, during which businesses and data collection practices have evolved a lot. But big data is not just that; it is now a much more advanced analytics game. It now includes predictive models and situational statistical algorithms, which businesses can use to utilize raw data to its full potential.
Big data analytics works by processing, cleaning, and analyzing collected raw data and making it useful for organizations. The process works in a series of steps: data collection, data processing, data cleaning, and data analysis. Now let’s understand the process in detail.
Data Collection is done with the help of advanced technology; it is collected from various sources such as social media, phone records, emails, google searches, consumer feedback, and mobile applications. All the data in raw form is then collected in data warehouses. Then comes the next step of Data Processing, which is done with the collected raw data. Data professionals do the data partition and configuration for analytical queries, and it is done in two ways,
1) Batch Processing is helpful for a business when it has enough time between collecting and analyzing data. It can process large data blocks over a while.
2) Steam Processing is a more complex process that can process small data batches at once. It shortens the delay period between analyzing and collecting data but is more expensive than a batch process. It is usually used when a quick decision is to be made.
Data cleaning is the third step in the process which filters the data and improves its quality. Then comes the fourth step and the last step of Data Analysis, which converts big data into a more useful format. This process takes time but, once completed, uses advanced analytics to transform big data into practical insights. The data analysis techniques which are commonly used are data mining, deep learning, and predictive analysis
As Big Data Analytics is the future of the 21st century, there are some courses that you can do to expand your knowledge and establish your career in data analytics.
1) BIG DATA HADOOP CERTIFICATE TRAINING COURSE offered by SimpliLearn allows mastering the concepts of big data tools, Hadoop framework, and methodologies.
2) INTRODUCTION TO DATA ANALYTICS FOR BEGINNERS offered by SimpliLearn introduces the fundamental concepts of data analytics through real-world industry-related cases and examples.
3) BIG DATA ANALYTICS WITH TELECOMMUNICATION offered by Udemy is a wholesome guide for business intelligence professionals, data scientists, and graduates in how the telecommunication industry uses this technology for its benefits and data analytics facts.
4) PYTHON FOR DATA ANALYSIS: PANDA AND NumPy, offered by Coursera, is a course that helps to understand the fundamentals of data analysis in python. It also offers high-performance, easy use structures, and data analysis tools.
5) MASTERING BIG DATA ANALYTICS WITH PySpark offered by Udemy is of great appeal to anyone familiar with machine learning concepts, including scientists, analysts, and people interested in scaling up their work with big data. At the end of the course, one can deploy PySpark at an industrial scale and perform proficient data analytics.
Edited By- Mahi Gupta
Published By- Pawan Rajput
