## Introduction to Data Science Workbook

The principal purpose of Data Science is to find patterns within data. It uses various statistical techniques to analyse and draw insights from the data. From data extraction, wrangling and pre-processing, a Data Scientist must scrutinize the data thoroughly.

Then, he has the responsibility of making predictions from the data. The goal of a Data Scientist is to derive conclusions from the data. Through these conclusions, he can assist companies in making smarter business decisions.

In the current world, whether you are a data scientist or a businessman or an undergraduate student data science is the study of crucial importance. There is no alternative to it.

Therefore, this extremely sophisticated and interesting data science workbook is a guarantee to your excellence into the field of data science.

The “Introduction to Data Science” course is its pre-requisite. It’s advised to first complete it before moving ahead to begin the workbook.

Let us have a quick overview  of the “Introduction to Introduction to Data Science Course” as well since it is a predecessor to this workbook.

S2 is an integrated development environment (IDE) of advanced analytics and data. Built using Java and NM Dev S2 has a suite of high-performance optimisation. It uses the Kotlin “glue” language to call a (large) number of Java Virtual Machine (JVM) based math, engineering, and data science libraries. Since the Kotlin is at the base of S2, you will learn basic Kotlin in the starting of the course. Furthermore, you would learn doing math with S2, data loading, storage, handling etc.

Linear Algebra – The x and y things, of the middle school. You remember them, right? And the vector operations the matrix algebra, the eigen values and the dot-cross products? This part of the course is a complete roller coaster ride right from the school algebra to the engineering mathematics and yet explained in such a sweet way that you would say, “Hey this finished so early?” Math has never been so easy before. If there is algebra there must be equations, and if there are equations there need to be their roots. The root finding using various algorithms and the liner interpolation along with its subsidiaries is the next frame of our data science movie. The data science is more about visualisation and analysis. And the curves and graphs are the best alternative to it. Therefore, the curve fitting and interpolation are one of the key chapters of the course. Covering all types of required graphs and their analysis is covered in a holistic manner. To students’ nightmare and teachers favourite the differentiation and integration are encountered next. Since the curves and their slopes require good knowledge of the integration and differentiation so this part of our studies is useful during drawing conclusions from the graph. Where there is differentiation there are differential equations both partial and ordinary differential equations. They need to be solved and solving them is not a cakewalk. Therefore, in a very elaborated approach for their solving, this part is covered. Prediction and probability to data science is like oxygen to the fire. The probability distribution, its graphs, the basic statistics, and the linear regression are the crucial elements of our studies of data science. The basics include mean, mode, variance etc. that constitute the statistics. The machine learning and further artificial intelligence are kind of subdomains of the data science . It forms the basis of all AI & ML algorithms and therefore towards the conclusion we would gain knowledge about the AI, ML, artificial neural network, and the optimisation problems. There are in total 86 topics with each one being handcrafted and meticulously designed take your learning experience to a different level as well as your knowledge to unreached heights. Following this course with sincerely and dedication is a proof of your farsightedness and visionary skills.

Having gone through the pre-requisite course in an eagle’s eye view here are the actual contents of “Introduction to Data Science Workbook”.

This course can be accessed via this link : Introduction to Data Science Workbook | NM DEV

Once you click and follow the above link it lands you up at the following page:

1. The RED Box – It shows your current status if you are enrolled in the course or not. Currently it is showing not enrolled. Once you register it turns to green. You can access the contents of the workbook only and only if you are enrolled.
2. The CYAN Box – It shows the price of the course which is currently \$10. Since this course is a limited time opportunity therefore the price might increase in near future. It is always best to enrol before the seats get filled.
3. The GREEN Box – It shows the status of the course, if it is available and open for booking. It is currently closed but very soon the slots are going to open. If you seriously want to excel in data science you cannot afford to miss any chance to grab this opportunity.

The course contents are in the following section. Mostly the course contents are the practice to the Introduction to Data Science Course.

1. Linear Algebra – (12 Quizzes)
1. Quiz 1 on Matrix Decompositions
2. Quiz 1 on Basic Vector Operations
3. Quiz 2 on Basic Vector Operations
4. Quiz 1 on Vector Space
5. Quiz 2 on Vector Space
6. Quiz 1 on Matrix Algebra
7. Quiz 2 on Matrix Algebra
8. Quiz 1 on Dot Product and Matrix Properties
9. Quiz 2 on Dot Product and Matrix Properties
10. Quiz 1 on Eigenvalues and Matrix Diagonalization
11. Quiz 2 on Eigenvalues and Matrix Diagonalization
12. Quiz 2 on Matrix Decompositions
1. Finding Roots of Equation – (4 Quizzes)
1. Quiz 1 on Functions, Graphs and Roots
2. Quiz 1 on Jenkins-Traub Algorithm
3. Quiz 1 on The Bisection Method
4. Quiz 1 on Brent’s Method
1. Finding Roots of System of Equations – (4 Quizzes)
1. Quiz 1 on Introduction to Linear Systems of Equations
2. Quiz 1 on Solving System of Linear Equations
3. Quiz 1 on Introduction to System of Non-linear equations
4. Quiz 1 on Solving Non-linear System of Equations with Newton’s Method
1. Curve Fitting and Interpolation – (5 Quizzes)
1. Quiz on Least Square Curve Fitting
2. Quiz on Interpolation
3. Quiz on Polynomial Interpolation
4. Quiz on Newtons Interpolation Method
5. Quiz 1 on Integrating Factor Method
1. Ordinary Differential Equations – (5 Quizzes)
1. Quiz 1 on Order and Degree of ODE
2. Quiz 1 on Initial Value Problem
3. Quiz 1 on Separable Equations
4. Quiz 1 on Euler’s Method
5. Quiz 1 on Runge-Kutta Method
1. Unconstrained Optimisation – (6 Quizzes)
1. Quiz on C2OptimProblem
2. Quiz on Brute Force Search
3. Quiz on Bracketing Methods
4. Quiz on Steepest Descent Methods
5. Quiz on Quasi-Newton Methods
6. Quiz on Conjugate Direction Methods
1. Basic Statistics – (4 Quizzes)
1. Quiz 1 on Rank, Quantile, Median, Min and Max
2. Quiz 1 on Skewness, Kurtosis and Moments
3. Quiz 1 on Covariance and Correlation
4. Quiz 1 on Mean and Variance
1. Random Number Generation – (3 Quizzes)
1. Quiz 1 on Uniform Random Number Generators
2. Quiz 1 on Sampling from Univariate Distribution
3. Quiz 2 on Sampling from Univariate Distribution
1. Linear Regression – (3 Quizzes)
1. Quiz 1 on Simple Linear Regression
2. Quiz 2 on Simple Linear Regression
3. Quiz 1 on Multiple Linear Regression
1. Machine Learning – (1 Quiz)
1. Quiz 1 on Boundary for Logistic regression

We sincerely hope that after going through the above article you must have realized the importance of this data science workbook. Therefore, what are you waiting for, just enroll as soon as possible once the slots open and be a leader in the field of data science.

Bye!

Visit our website http://Nm.dev/ and do check out our S2 portal http://Nm.dev/s2.

## Inspiration behind NM Dev

Scripting languages (such as R/MATLAB/Python etc.) come with huge libraries for mathematics built-in and provide users with the ability to interact with data dynamically. On top of that they are very simple to use, even for someone with no programming experience, making them extremely effective prototyping tools.

However, after prototyping is done, the code often has to be translated into production code (Such as Java/C#/C++ etc.) so that it runs faster (up to 100 times faster) and more stably and for integration with the hosting framework, application, hardware and software.

The problem with the translation process is that it is difficult to ensure that the prototype and production code produce the same outputs. On top of that, the prototyping code is often written by mathematicians who know little about programming and the production code is written by programmers who know little about the mathematical models. Moreover, the mathematical libraries available to the prototyping languages are often not available to the production languages. This results in a painful and time consuming translation process.

Running on the S2 IDE, NM Dev solves this translation problem by combining the strengths of the prototyping and production languages into a single integrated language that allows you to test and produce code all at once. NM Dev is a high performance (faster than any scripting language) and high availability (handles errors and exceptions) language that has a large library of numerical algorithms and can run anywhere with a Java Virtual Machine (JVM), including vehicles, microwaves, watches and mobile phones.

Beyond just streamlining the production process, NM Dev also supports big data analytics and artificial intelligence as well as visualization of huge amounts of data.

Currently, NM Dev math library covers all of classical mathematics (Linear Algebra, Calculus, Statistics, Time Series Analysis etc.) and also has the most comprehensive optimization library, covering all aspects of classical mathematical optimization. In addition, NM Dev is the fastest math library available to date, with our matrix multiplication being 180 times faster than Apache and 14 times faster than R. (suanshu-3.3.0 has been replaced by NM Dev)

If you want to streamline your app development process and increase performance, then NM Dev is for you. Click here to try it out.