Build a Career in Data Analytics and Data Science
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Learn Deployement Of Data Science & Machine Learning Web App With Python & Django In Heruko, Streamlit
Have a great intuition of many data science models
Make robust data science models
Learn exploratory data analysis
Implement Machine Learning Algorithms
Build machine learning models
Knowledge Of Data ScienceKnowledge Of Python
“Data science is the transformation of data using mathematics and statistics into valuable insights, decisions, and products”
Today, data science is employed across a broad range of industries and aids in various analytical problems. For example, in marketing, exploring customer age, gender, location, and behavior allows for making highly targeted campaigns, evaluating how much customers are prone to make a purchase or leave. In banking, finding outlying client actions aids in detecting fraud. In healthcare, analyzing patients’ medical records can show the probability of having diseases, etc.
The data science landscape encompasses multiple interconnected fields that leverage different techniques and tools.
There’s a difference between data mining and very popular machine learning. Still, machine learning is about creating algorithms to extract valuable insights, it’s heavily focused on continuous use in dynamically chag environments and emphasizes adjustments, retraining, and updating of algorithms based on previous experiences. The goal of machine learning is to constantly adapt to new data and discover new patterns or rules in it. Somes it can be realized without human guidance and explicit reprogramming.
Machine learning is the most dynamically developing field of data science today due to a number of recent theoretical and technological breakthroughs. They led to natural language processing, image recognition, or even the generation of new images, music, and texts by machines. Machine learning remains the main “instrument” of building artificial intelligence.
Who this course is for:Bners In Data Science