• Getting Started and Selecting & Retrieving Data with Structured Query language (SQL)
• Intro to SQL for Data Science
• Filtering, Sorting, and Calculating Data with SQL
• Subqueries and Joins in SQL
• Modifying and Analyzing Data with SQL
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Structured Query language (SQL) is the standard language for dealing with Databases. The basic things that you can do by SQL are optimization and maintenance of databases. SQL statements are used to perform different kinds of tasks such as, retrieve data from a database, and update data on a database. There are many relational database management systems that use SQL are: Sybase, Oracle Microsoft SQL Server, Ingres, Access etc

To be an effective and good data scientist professional, you must know how to extract and wrangle data from databases using a language called SQL.  This course teaches syntax in SQL used by many types of databases.
This course is designed to give you the fundamentals and basics of SQL and working with data so that you became master of analyzing data for data science purposes.

​ You will begin to ask the right questions and come up with good answers that add value to your current position in the industry. You were able to deliver valuable insights for your organization. This course starts with the basics of Structured Query language and covers all the knowledge and skills that are there in SQL. After the completion of the course You'll start to work with different types of data like numbers and strings and discuss methods to pair and filterdown your results.

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.
Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.
With this book, you’ll learn:

• Why exploratory data analysis is a key preliminary step in data science
• How random sampling can reduce bias and yield a higher quality dataset, even with big data
• How the principles of experimental design yield definitive answers to questions
• How to use regression to estimate outcomes and detect anomalies
• Key classification techniques for predicting which categories a record belongs to
• Statistical machine learning methods that “learn” from data
• Unsupervised learning methods for extracting meaning from unlabeled data

Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on.
This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks.
By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically.
What you will learn

• Analyze the transition from a data developer to a data scientist mindset
• Get acquainted with the R programs and the logic used for statistical computations
• Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more
• Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis
• Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks
• Get comfortable with performing various statistical computations for data science programmatically

Data scientists are changing the way big data is used in different institutions. Big data is everywhere, but without the right person to interpret it, it means nothing. So where do business find these people to help change their business? 
You could be that person! 
It has become a universal truth that businesses are full of data. With the use of big data, the US healthcare could reduce their healthcare spending by $300 billion to $450 billion. It can easily be seen that the value of big data lies in the analysis and processing of that data, and that's where data science comes in. 
Grab your copy today and learn: 

• In-depth information about what data science is and why it is important
• The prerequisites you will need to get started in data science
• What it means to be a data scientist
• The roles that hacking and coding play in data science
• The different coding languages that can be used in data science
• Why python is so important
• How to use linear algebra and statistics
• The different applications for data science
• How to work with the data through munging, cleaning, and more
• And much more

The use of data science adds a lot of value to businesses, and we will continue to see the need for data scientists grow. As businesses and the Internet change, so will data science. This means it's important to be flexible. 
When data science can reduce spending costs by billions of dollars in the healthcare industry, why wait to jump in? 
If you want to get started in a new, ever-growing career, don't wait any longer. Download your copy now!

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• Understand the basics of probability
• Understand how regression models work
• Understand how to use various statistical distributions
• Understand Chi Squared Tests
• Apply statistical methods and hypothesis testing to business problems
• Be able to understand different types of data
• Implement one way and two way ANOVA

If you want to succeed in business and the data science field this course on probability and statistics is for you, we cover all the topics related to statistics and probability.


This course is all about practicals and implementations of statistics in the real world problems; course contains minimum theory and gives more importance to build practical knowledge and core concept about the subject. Each section has dummy problems, in course quizzes, and assessment tests.

The course starts with the very basics of data, building and understanding of how bivariate data sources can be related to each other, dispersion of data, understanding how to examine it with measurements of central tendency.

After completion of basics we’ll dive into probability, learning about permutation and combination, how to apply bayes theorem and whole concept about conditional probability.

Then we’ll move on to discussing and creating a solid base foundation of understanding how to work with binomial, uniform, poisson, and normal distributions and in this course we will learn and develop skills about the most common distributions found in statistics.

The next topic we’ll be covering is all about statistics, starting with practical usage, applying what we’ve learned so far to real world business cases, including the student's T distribution and hypothesis testing. The last topic of this course consist of 3 sections on advanced topics, such as analysis of variance (ANOVA), performing chi squared analysis and finally understanding regression analysis.



This course includes HD quality video lectures with clear explanations. High quality animations are also there in the videos; we also include extensive case studies to show you how to apply this knowledge to the real world.

​• ​How to manipulate vectors and numbers, and will understand objects and classes.
• How to create data structures in R: arrays, matrices and vectors; lists and data frames.
• How to use R as a statistical environment following many examples.
• How to estimate create and interpret ANOVA, GLM, GAM and regression statistical models with number of examples on each topics.
• How to create statistical and other visualizations using both the base and gplot graphics capabilities in R.

This course is on the topic “Comprehensive Statistics and Data Science with R”, This course is all about introduction to R. This Course is totally based on the authoritative documentation known as “An Introduction to R" is manually produced with each new release on R by the Comprehensive R Archive Network (CRAN) development core team. These are the people who are mainly responsible for writing, testing, producing and releasing the R code to the general public by way of the CRAN mirrors. It is a rich, compound and detailed course comprises of 10-sessions which covers each and every part of Data science and Statistics in the contemporary 105-page CRAN manual. The ten sessions follows the outline and covers all the necessary information that is present in “the An Introduction to R” online manual.
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The ten sessions specifically instruct with respect to the following user topics:

1. Introduction to R; Inputting data into R
2. Simple manipulation of numbers and vectors
3. Objects, their modes and attributes
4. Arrays and matrices
5. Lists and data frames
6. Writing user-defined functions
7. Working with R as a statistical environment
8. Statistical models and formulae; ANOVA and regression
9. GLMs and GAMs
10. Creating statistical and other visualizations with R

• Introduction to Data Science in Python
• Applied Plotting, Charting & Data Representation in Python
• Applied Machine Learning in Python
• Applied Text Mining in Python
​• Applied Social Network Analysis in Python

These courses introduce data science through the Python programming language to learners at the University of Michigan. This is for them who have basic skills in Python or another programming background. Courses1,2 and 3 should be taken in order and prior to any other course in the specialization. After completing those three courses, courses 4 and 5 can be taken in any order. To earn a certificate all 5 courses are required to be completed.
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Introduction to Data Science in Python(Course1)-This course will introduce the learner to the basics of the Python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating CSV files, and the Numpy library. 
Applied Plotting, Charting & Data Representation in Python(Course2)-This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the Matplotlib library.
Applied Machine Learning in Python(Course3)-This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods.
Applied Text Mining in Python(Course4)-This course will introduce the learner to text mining and text manipulation basics; an overview of the NLTK framework for manipulating text.
Applied Social Network Analysis in Python(Course5)-This course will introduce the learner to network analysis through tutorials using the NetworkX library. 

If you want to master the skills to perform the basic statistical analyses in the R program, you have come to the right place. In this course, explained visually, step by step how to calculate and figure out the statistical indicators in R, to build a scatter-plot chart, how to build a cross-table, and how to calculate a simple statistical test like one-sample test. Everything is well presented and course is uniformly distributed in this particular course, now you don’t have to search the web endlessly in order to find best course for statistical analyses.

​In the beginning of the course you will learn how to manipulate data in R, to prepare it for the analysis: how to recode variables, how to filter your data frame, and compute new variables for the analysis.

Afterwards, we will start with computing the main statistical figures in R that includes standard deviation, mean, median, skewness, , kurtosis etc, both in the subgroups and whole population.

Then you will learn easy visualization and reading of data using charts and tables. So we will build cross-tables and tables, as well as cumulative frequency charts, histograms, column and mean plot charts, boxplot, scatterplot, charts etc.

​This is a book regarding data science with Python. A python is an excellent tool for several analyzers because of its libraries for manipulating, storing and gaining insight from data. Python code is ideal for tackling day-to-day problems like- visualizing different types of data; manipulating, transforming, and cleaning data; using data to build machine learning or statistical models. It is terribly merely that it should have a reference for scientific computing in Python. It is meant to assist Python users by learning the way to use Python’s data science stack libraries like- IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related tools—to effectively store, manipulate, and gain insight from data.
 
What you will learn:
 
• Jupyter and IPython – In several, Python using data scientists work with these packages and provide the computational environment.
• NumPy -This library provides the array object for economical storage and manipulation of dense data arrays in Python.
• Pandas- For efficient storage and manipulation of labeled/columnar data in Python the Data Frame object is provided by the library.
• Matplotlib- Capabilities of a versatile range of data visualizations in Python is provided by the library.
• Scikit-Learn- This library provides economical and clean Python with implementations of the foremost necessary and established machine learning algorithms.
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​This book will help you in getting familiar with data science using Python 3.5; Save time (and effort) with all the essential tools explained and additionally produce effective data science projects which can avoid common problems with the help of hints prescribed by experiences and examples. Get trendy vision into the core of Python data which incorporates the latest versions of NumPy, Jupyter notebooks, sci-kit-learn, and pandas. This book gives the complete overview of all the visualization and deployment instruments which makes it an easier option to present your results to an audience of both business users and data science experts; principal of machine learning algorithms and graph analysis techniques.
 
What you will learn:
 
• By using a Python scientific environment on Windows, Mac, and Linux how you can set up your data science toolbox
• Get data prepared for your data science project
• Explore, Manipulate and fix data so as to resolve data science problems
• For testing your data science hypotheses how you can set up an experimental pipeline
• For your data science tasks how to select the foremost effective and scalable learning algorithm
• Forgetting the best performance of how to optimize your machine learning models
• Taking advantage of interconnections and links in your data by exploring and the cluster of graphs.
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​In this book, Master data science uses Python and its libraries in many ways. This comprehensive guide helps you to move beyond the enhancement and transform the theory which provides a hands-on and advanced study of data science using python, and also easy-to-follow. Data science is comparatively a new cognitive content which is employed by various organizations to produce data-driven decisions. Informing high-end visualizations in Python matplot library is used and also uncovers the basics of machine learning. All the topics covered in this book can be used in real-world circumstances.
 
What You Will Learn:
 
•Perform linear algebra and manage data in Python; evaluate and apply linear and logistic regression techniques in various application techniques for estimating the relationships among variables.
•Derive assumptions from the analysis by mining data to reveal hidden patterns and trends and performing inferential statistics
• Resolve data science issues in Python
•With the help of various collaborative filtering algorithms, how you can build recommendation engines
• Apply the ensemble ways to boost your predictions
• For handling data at large scale how we work with large data technologies
•Produce mine for patterns and data visualizations.
•The four basics of Data Science with Python having advanced techniques like- data mining, machine learning, data visualization, and data analysis
•Perform clustering together with an analysis of unstructured data with completely different text mining techniques and to invest the power of Python in big data analytics.

​The book begins with setting up the environment for Anaconda platform so as to make it accessible for tools and frameworks like- Jupyter, pandas, matplotlib, Python, R, Julia, and more. Anaconda is an open source platform which brings along the simplest tools for data science professionals with more than 100 popular packages supporting Python, Scala, and R languages.
Hands-On Data Science with Anaconda gets you started with Anaconda and demonstrates how to perform data science operations in the real world. It is ideal for data analysts and data science professionals who want to boost the efficiency of their data science applications by using the best libraries in multiple languages. Basic programming knowledge with R or Python and introductory knowledge of linear algebra is expected.

What you will learn:
·        Perform cleaning, sorting, classification, clustering, regression, prediction, and building machine learning models and optimizing them and dataset modeling using Anaconda
·        Use the package manager conda and discover, install, and use functionally efficient and scalable packages
·        Get comfortable with heterogeneous data exploration using multiple languages within a project
·        Discover and share packages, notebooks, and environments, and use shared project drives on Anaconda Cloud
·        Tackle advanced data prediction issues
 
·        Explore all the necessities information of data science and linear algebra to perform data science tasks using packages such as SciPy, contrastive, and many more.
·        Find out how to visualize data using the packages available for Julia, Python, and R. Analyze your data efficiently with the foremost powerful data science stack.
 

Practical Data Science with R shows helpful statistical techniques for everyday business situations and ways for using the R programming language. Without plenty of academic theory or advanced mathematics, this R language is associated with tools which give simple ways to tackle with day-to-day data science tasks .

In this book, you will learn the statistical analysis techniques to explain examples which are based mostly on decision support, business intelligence, and marketing. This is the book for you if you are a data scientist, want to be a data scientist, or want to work with data scientists. 

​This is a good “what next” book for analysts and programmers wanting to know more about machine learning and data wrangling. Concept of this book is to present data science from a pragmatic, practice-oriented viewpoint. 

​What you will learn
 
• How to work as a data scientist. Learn how important listening, collaboration, honest presentation, and iteration are to and what we do.
• The key significance of the book is loading data, collecting requirements, validating models, examining data, deploying models to production, building models and documenting.
• This provides over 10 significant examples of datasets and demonstrates the concepts which are discussed with fully worked exercises using standard R methods.
• It will demonstrate all the preparatory steps necessary for any real-world project. Every result and almost every graph in the book is given as a fully worked example.
• It is scrupulously correct on statistics, but presents topics in the context and order a practitioner worries about them.

This book focuses primarily on R, but also uses several other domain-specific languages (DSLs) and even touches on languages such as the UNIX shell and C, also illustrate the process by which programmers approach a problem and implement the solution in different ways. This book has 3 parts, with each part having a general theme.

Part I contains case studies that involve reading and transforming raw data, manipulating and visualizing them, and then using statistical techniques to try to solve a problem or understand relationships between variables.
Part II focuses on using simulation to understand stochastic processes for their own sake and also explore how to use simulation to model interesting situations.
Part III explores different data technologies. These include databases, visualization with KML, and scraping data from Web pages with HTTP requests and text processing.

The scope of this book is wide, covering three main topics:
 • Applications of R to specific disciplines
• For the study of topics of the statistical methodology by Using R
• The development of R also including building packages, programming, and graphics

What you will learn
• Non-standard data formats (robot logs, email messages)
 • Text processing and regular expressions
 • Newer/less-traditional technologies (Web scraping, Web services, JSON, XML, HTML, KML and Google Earth™)
• Statistical methods (classification trees, k-nearest neighbors, naïve Bayes)
 • Visualization and exploratory data analysis; • relational databases and SQL
• Simulation
• Implementing algorithms
• Large data and efficiency
• Software design, development, and testing
 • Using and interfacing to other languages such as the UNIX shell, C, and Python.  
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This book aims to teach you how to begin performing the data science tasks by taking advantage of R's powerful ecosystem of packages. R is the most widely used programming language and when used with data science, it can be a great combination to solve the problems involved with varied data sets in the real world. For statistical simulation to the users, it will provide a methodological and computational framework. This book is for them who want to learn about the computer-intense Monte-Carlo methods, the advanced features of R and computational tools for statistical simulation. Good knowledge of R programming is assumed/required.
 
You will learn five different simulation techniques in-depth using real-world case studies which are as follows-
1. Monte Carlo; 2. Discrete Event Simulation;
3. System Dynamics; 4 .Agent-Based Modeling;
5. Resampling
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It teaches the essential and fundamental concepts in statistical modeling and simulation. For explaining the statistical computing methods, it takes a practical and hands-on approach and gives advice on the usage of these methods. It provides computational tools to help you in solving common problems in statistical simulation and computer-intense methods. This book helps in uncovering the large-scale patterns of complex systems where interdependencies and variation are critical.
 
What You Will Learn
 
·        Advanced R features to extract insights from your data and to simulate data
·        How simulation project can be plan and structure to aid in the presentation of results and also in the decision-making process.
·        To simulate distributions, data sets, and populations is done by seeing random number simulation
·        For solving scientific and real-world problems by using design statistical solutions with R
·        High-performance computing and advanced data manipulation
·        Comprehensive coverage of several R statistical packages like simPop, boot, VIM, and many more.