Data Science as a Career and Intro to R
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This document discusses data science as a career option and provides an overview of the roles of data analyst, data scientist, and data engineer. It notes that data analysts solve problems using existing tools and manage data quality, while data scientists are responsible for undirected research and strategic planning. Data engineers compile and install database systems. The document also outlines the typical salaries for each role and discusses the growing demand for data science skills. It provides recommendations for learning tools and resources to pursue a career in data science.
![1 3 5 7 10 12 14 20
Timeline(Weeks)[Beginers]
Learn the
Language -
R/Python
Start Doing
Hackathons/Pet Projects
Practice the
Langauge, Finish
Intro in ML
Do more advance
ML, start optimizing
your code.Start
reading git commits](https://image.slidesharecdn.com/smp-rintrosession-170304083548/85/Data-Science-as-a-Career-and-Intro-to-R-17-320.jpg)
Data Science as a Career and Intro to R
- 1. A Discussion on Data Science as a Career option -By Anshik - Under Student Mentorship Prog.
- 3. Overview As data has multiplied, so has the ability to collect, organize, and analyze it. Data storage is cheaper than ever, processing power is more massive than ever, and tools are more accessible than ever to mine huge amount of available data for business intelligence. The McKinsey Global Institute predicted that by 2018 the U.S. could face a shortage of 1.5 million people who know how to leverage data analysis to make effective decisions. Enter: you, taking stock of your three main career options: data analyst, data scientist, and data engineer
- 4. Career options and difference between them Data Analyst (1.6l - 8L) Solve problems using existing tools No mathematical or research background required. Manage quality of scraped data, querying databases and serve data as visualization. Data Scientist(3.5L - 18L) Similar to data analyst in many aspects. Responsible for doing undirected research and tackle open-ended problems and questions. Data analyst summarizes the past; a data scientist strategizes for the future Data Engineer(3L - 21L) Does groundwork for the former two. Responsible for compiling and installing database systems, writing complex queries, scaling to multiple machines, and putting disaster recovery systems into place.
- 5. Should you put in the time and effort? ●
- 6. What do you think? Data set that contains the salaries of people who work at an organization. -- What questions can be formed? -- What Interpretations can be made?
- 7. 1.Most of the positions sought Masters / PhD students (especially in statistics). Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt The competition: ● Lorem ipsum ● Dolor sit amet
- 8. 2.Learning from MOOCs is not easy and is time-consuming Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt The competition: ● Lorem ipsum ● Dolor sit amet
- 9. 3.Condense what you know in presentable manner. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt The competition: ● Lorem ipsum ● Dolor sit amet
- 10. On a brighter side...
- 11. Srikanth Velamakanni,CEO of CA headquartered Fractal Analytics: “In the next few years, the size of the analytics market will evolve to at least one-thirds of the global IT market from the current one-tenths”
- 13. Key points ● Huge Job Opportunities & Meeting the Skill Gap ● Salary Aspects ● The Rise of Unstructured and Semistructured Data Analytics ● Used Everywhere
- 14. Total Enterprise Data Growth 2005-2015 The way we capture, store, analyze, and distribute data is transforming. Deduplication,compression, and analysis tools are lowering costs.
- 15. Tools and Resources Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt The competition: ● Lorem ipsum ● Dolor sit amet
- 16. Categories and Links Books ISLR, R for Dummies, Advanced R, Machine learning for Hackers(Py), NLP with Python Websites and Blogs Analytics Vidhya, Rbloggers, Kaggle Scripts, CrowdAnalytics, students.brown.edu, github.io Statistics and Linear Algebra Inferential and Descriptive statistics by Udacity, MSR sir’s Prob & stats Slide, Khan Acad(Lin.Alg) Machine Learning and AI Andrew Ng's ML Class, John Hopkins Data Analysis, Deepak Khemani(AI-nptel) Data Storage and Visualization MongoDB(Udacity), D3.js documentation and wiki
- 17. 1 3 5 7 10 12 14 20 Timeline(Weeks)[Beginers] Learn the Language - R/Python Start Doing Hackathons/Pet Projects Practice the Langauge, Finish Intro in ML Do more advance ML, start optimizing your code.Start reading git commits
- 18. Intro To ML & R Installing Packages :- To install a package, use the install.packages() function. Once a package is installed, it must be loaded into your current R session before being used using library() or require(). Think of this as taking the book off of the shelf and opening it up to read. TIP :- Use require function for loading a package as it throws false if package is not found. Data Types :- R has a number of basic data types. 1. Numeric :- Also known as Double. The default type when dealing with numbers. Examples: 1, 1.0, 42.5 2. Integer: - Examples: 1L, 2L, 42L 3. Complex : - Example: 4 + 2i 4. Logical : - Two possible values: TRUE and FALSE, you can also use T and F, but this is not recommended. NA is also considered logical. 5. Character :- Examples: "a", "Statistics", "1 plus 2."
- 20. R Object oriented System S3 Lacks formal definition Objects are created by setting the class attribute Attributes are accessed using $ Methods belong to generic function Follows copy-on-modify semantics S4 Class defined using setClass() Objects are created using new() Attributes are accessed using @ Methods belong to generic function Follows copy-on-modify semantics Reference Classes Class defined using setRefClass() Objects are created using generator functions Attributes are accessed using $ Methods belong to the class Does not follow copy-on-modify semantics
- 21. Simple Linear Reg. using R
- 22. We will use inbuilt Cars dataset in R-base Data gathered during the 1920s about the speed of cars and the resulting distance it takes for the car to come to a stop. Objective :- How far a car travels before stopping, when traveling at a certain speed?
- 23. What sort of function should we use for f(X)[Y=f(X) +e) for the cars data? - A Horizontal Line? We see this doesn’t seem to do a very good job. Many of the data points are very far from the orange line representing cc . This is an example of underfitting. - Make f(x) depend on x - As speed increases, the distance required to come to a stop increases. There is still some variation about this line, but it seems to capture the overall trend.
- 25. Assumptions of Linear Regression LINE Linear. The relationship between Y and x is linear, of the form β0+β1x . Independent. The errors ϵ are independent. Normal. The errors, ϵ are normally distributed. That is the “error” around the line follows a normal distribution. Equal Variance. At each value of x , the variance of Y is the same, σ2 . We have to find a line that minimize sum of all squared distances from point to line.
- 26. lm() stop_dist_model = lm(dist ~ speed, data = cars) The abline() function is used to add lines of the form a+bx to a plot. (Hence abline.) When we give it stop_dist_model as an argument, it automatically extracts the regression coefficient estimates ( β̂0 and β̂1) and uses them as the slope and intercept of the line. Here we also use lwd to modify the width of the line, as well as col to modify the color of the line. lm() function returns an object of class lm() We can access the members using $ operator > names(stop_dist_model) > stop_dist_model$residuals Use summary() to summarize the output for linear regression.The summary() command also returns a list, and we can again use names() to learn what about the elements of this list. > names(summary(stop_dist_model)) > summary(stop_dist_model)$r.squared Use predict function to predict output for certain input values > predict(stop_dist_model, data.frame(speed = c(8, 21, 50)))
- 27. Thank You -Anshik Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt The competition: ● Lorem ipsum ● Dolor sit amet 8826274098 (Watsapp)