Machine Learning and Data Science with Python
Machine Learning and Data Science with Python / get udemy coupon code
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What you'll learn
- Data Science with Python
- Python libraries such as Numpy, Matplotlib, Pandas, Scipy, Scikit-learn etc
- Data Science using real world examples
- Introduction to Data Science
- Machine Learning using real world examples
- Introduction to Machine Learning
- Do It Yourself Python tutorials
- Live coding sessions
Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems.
At the core is data. Troves of raw information, streaming in and stored in enterprise data warehouses. Much to learn by mining it. Advanced capabilities we can build with it. Data science is ultimately about using this data in creative ways to generate business value
How do data scientists mine out insights? It starts with data exploration. When given a challenging question, data scientists become detectives. They investigate leads and try to understand patterns or characteristics within the data. This requires a big dose of analytical creativity.
Then as needed, data scientists may apply the quantitative technique in order to get a level deeper – e.g. inferential models, segmentation analysis, time series forecasting, synthetic control experiments, etc. The intent is to scientifically piece together a forensic view of what the data is really saying.
This data-driven insight is central to providing strategic guidance. In this sense, data scientists act as consultants, guiding business stakeholders on how to act on findings.
A common personality trait of data scientists is they are deep thinkers with intense intellectual curiosity. Data science is all about being inquisitive – asking new questions, making new discoveries, and learning new things. Ask data scientists most obsessed with their work what drives them in their job, and they will not say "money". The real motivator is being able to use their creativity and ingenuity to solve hard problems and constantly indulge in their curiosity.
Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.
The five stages of the data science life cycle: Capture, (data acquisition, data entry, signal reception, data extraction); Maintain (data warehousing, data cleansing, data staging, data processing, data architecture); Process (data mining, clustering/classification, data modeling, data summarization); Analyze (exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis); Communicate (data reporting, data visualization, business intelligence, decision making).
Effective data scientists are able to identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions. These skills are required in almost all industries, causing skilled data scientists to be increasingly valuable to companies.