Pack of 10 - Data Science Certification Bundle

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Courses Included In The BundleLearn By Example: Statistics and Data Science in R

Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. 

This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples. 

Let’s parse that.

  • Gentle, yet thorough: This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualising yo…

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Nog niet gevonden wat je zocht? Bekijk deze onderwerpen: Data Science, Databases, Big Data, Datavisualisatie en Data Analyse.

Courses Included In The BundleLearn By Example: Statistics and Data Science in R

Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. 

This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples. 

Let’s parse that.

  • Gentle, yet thorough: This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualising your findings. 
  • Data Science, Statistics and R: This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R. 
  • Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context. 

What's Covered:

  • Data Analysis with R: Datatypes and Data structures in R, Vectors, Arrays, Matrices, Lists, Data Frames, Reading data from files, Aggregating, Sorting & Merging Data Frames
  • Linear Regression: Regression, Simple Linear Regression in Excel, Simple Linear Regression in R, Multiple Linear Regression in R, Categorical variables in regression, Robust regression, Parsing regression diagnostic plots
  • Data Visualization in R: Line plot, Scatter plot, Bar plot, Histogram, Scatterplot matrix, Heat map, Packages for Data Visualisation : Rcolorbrewer, ggplot2
  • Descriptive Statistics: Mean, Median, Mode, IQR, Standard Deviation, Frequency Distributions, Histograms, Boxplots
  • Inferential Statistics: Random Variables, Probability Distributions, Uniform Distribution, Normal Distribution, Sampling, Sampling Distribution, Hypothesis testing, Test statistic, Test of significance

Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?

  • Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role
  • Yep! Engineers who want to understand basic statistics and lay a foundation for a career in Data Science
  • Yep! Analytics professionals who have mostly worked in Descriptive analytics and want to make the shift to being modelers or data scientists
  • Yep! Folks who've worked mostly with tools like Excel and want to learn how to use R for statistical analysis

Worth $ 49.99 $9.99Data Science with R

This course introduces R programming environment as a way to have hands-on experience with Data Science. It starts with a few basic examples in R before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.

Worth $ 74.99 $14.99Python for Data Science

This course is for those who want to step into Data Science domain, specially into Machine Learning, though I will be covering everything in deep and from the scratch.

This course will take your knowledge in Python from A to Z in a day ( Ofcourse if you can sit in one go ).

I have covered everything from "Hello World" in Python to all the required "Libraries like pandas and numpy"

Worth $ 249.99 $49.99Getting Started with Data Sciences

In the age of Data Revolution and rapid technological advancement, do not be left behind. Data Sciences has come to the fore as a must have knowledge whether you are a Businessman wanting to invest in new Products and Services OR whether you are working on any particular domain. With so much data available from almost any kind of device we use in our daily lives, the application of Data Sciences is growing at an exponential pace.

This course provides an introduction to Data Sciences. The goal of this short course is to expose as many areas of Data Sciences as possible within 1 hour. Once you are aware of these topics, you can study further any specific topic or all of the topics.

The course is designed for CxO and other Decision Makers who want to invest their money in taking advantage of this Data Revolution. This course is also meant for Students, Researchers and almost everyone from any work of life so that they are able to understand what is the upcoming world going to be.

Worth $ 49.99 $9.99DATA SCIENCE with MACHINE LEARNING and DATA ANALYTICS

DATA SCIENCE with MACHINE LEARNING and DATA ANALYTICS using R Programming, PYTHON Programming, WEKA Tool Kit and SQL.

This course is designed for any graduates as well as Software Professionals who are willing to learn data science in simple and easy steps using R programming, Python Programming, WEKA tool kit and SQL.

Data is the new Oil. This statement shows how every modern IT system is driven by capturing, storing and analysing data for various needs. Be it about making decision for business, forecasting weather, studying protein structures in biology or designing a marketing campaign. All of these scenarios involve a multidisciplinary approach of using mathematical models, statistics, graphs, databases and of course the business or scientific logic behind the data analysis. So we need a programming language which can cater to all these diverse needs of data science. R and Python shines bright as one such language as it has numerous libraries and built in features which makes it easy to tackle the needs of Data science.

In this course we will cover these the various techniques used in data science using the R programming, Python Programming, WEKA tool kit and SQL.

The most comprehensive Data Science course in the market, covering the complete Data Science life cycle concepts from Data Collection, Data Extraction, Data Cleansing, Data Exploration, Data Transformation, Feature Engineering, Data Integration, Data Mining, building Prediction models, Data Visualization and deploying the solution to the customer. Skills and tools ranging from Statistical Analysis, Text Mining, Regression Modelling, Hypothesis Testing, Predictive Analytics, Machine Learning, Deep Learning, Neural Networks, Natural Language Processing, Predictive Modelling, R Studio, programming languages like R programming, Python are covered extensively as part of this Data Science training.

Who this course is for:

  • All graduates are eligible to learn this course

Worth $ 49.99 $9.99Machine Learning and Data Science using Python for Beginners

Machine Learning and Data Science for programming beginners using python with scikit-learn, SciPy, Matplotlib & Pandas.

Hi.. Hello and welcome to my new course, Machine Learning with Python for Dummies. We will discuss about the overview of the course and the contents included in this course.

Artificial Intelligence, Machine Learning and Deep Learning Neural Networks are the most used terms now a days in the technology world. Its also the most mis-understood and confused terms too.

Artificial Intelligence is a broad spectrum of science which tries to make machines intelligent like humans. Machine Learning and Neural Networks are two subsets that comes under this vast machine learning platform.

Lets check what's machine learning now. Just like we human babies, we were actually in our learning phase then. We learned how to crawl, stand, walk, then speak words, then make simple sentences.. We learned from our experiences. We had many trials and errors before we learned how to walk and talk. The best trials for walking and talking which gave positive results were kept in our memory and made use later. This process is highly compared to a Machine Learning Mechanism.

Then we grew young and started thinking logically about many things, had emotional feelings, etc. We kept on thinking and found solutions to problems in our daily life. That's what the Deep Learning Neural Network Scientists are trying to achieve. A thinking machine.

But in this course we are focusing mainly in Machine Learning. Throughout this course, we are preparing our machine to make it ready for a prediction test. Its Just like how you prepare for your Mathematics Test in school or college. We learn and train ourselves by solving the most possible number of similar mathematical problems. Lets call these sample data of similar problems and their solutions as the 'Training Input' and 'Training Output' Respectively. And then the day comes when we have the actual test. We will be given new set of problems to solve, but very similar to the problems we learned, and based on the previous practice and learning experiences, we have to solve them. We can call those problems as 'Testing Input' and our answers as 'Predicted Output'. Later, our professor will evaluate these answers and compare it with its actual answers, we call the actual answers as 'Test Output'. Then a mark will be given on basis of the correct answers. We call this mark as our 'Accuracy'. The life of a machine learning engineer and a data-scientist is dedicated to make this accuracy as good as possible through different techniques and evaluation measures.

Here are the major topics that are included in this course. We are using Python as our programming language. Python is a great tool for the development of programs which perform data analysis and prediction. It has tons of classes and features which perform the complex mathematical analysis and give solutions in simple one or two lines of code so that we don't have to be a statistic genius or mathematical Nerd to learn data science and machine learning. Python really makes things easy.

These are the main topics that are included in our course:

  • System and Environment preparation
  • Installing Python and Required Libraries (Anaconda)
  • Basics of python and sci-py
  • Python, Numpy , Matplotlib and Pandas Quick Courses
  • Load data set from csv / url
  • Load CSV data with Python, NumPY and Pandas
  • Summarize data with description
  • Peeking data, Data Dimensions, Data Types, Statistics, Class Distribution, Attribute Correlations, Univariate Skew
  • Summarize data with visualization
  • Univariate, Multivariate Plots
  • Prepare data
  • Data Transforms, Rescaling, Standardizing, Normalizing and Binarization
  • Feature selection – Automatic selection techniques
  • Univariate Selection, Recursive Feature Elimination, Principle Component Analysis and Feature Importance
  • Machine Learning Algorithm Evaluation
  • Train and Test Sets, K-fold Cross Validation, Leave One Out Cross Validation, Repeated Random Test-Train Splits
  • Algorithm Evaluation Metrics
  • Classification Metrics - Classification Accuracy, Logarithmic Loss, Area Under ROC Curve, Confusion Matrix, Classification Report
  • Regression Metrics - Mean Absolute Error, Mean Squared Error, R 2
  • Spot-Checking Classification Algorithms
  • Linear Algorithms - Logistic Regression, Linear Discriminant Analysis
  • Non-Linear Algorithms - k-Nearest Neighbours, Naive Bayes, Classification and Regression Trees, Support Vector Machines
  • Spot-Checking Regression Algorithms
  • Linear Algorithms -  Linear Regression, Ridge Regression, LASSO Linear Regression and Elastic Net Regression
  • Non-Linear Algorithms - k-Nearest Neighbours, Classification and Regression Trees, Support Vector Machines
  • Choose The Best Machine Learning Model
  • Compare Logistic Regression, Linear Discriminant Analysis, k-Nearest Neighbours, Classification and Regression Trees, Naive Bayes, Support Vector Machines
  • Automate and Combine Workflows with Pipeline
  • Data Preparation and Modelling Pipeline
  • Feature Extraction and Modelling Pipeline
  • Performance Improvement with Ensembles
  • Voting Ensemble
  • Bagging: Bagged Decision Trees, Random Forest, Extra Trees
  • Boosting: AdaBoost, Gradient Boosting
  • Performance Improvement with Algorithm Parameter Tuning
  • Grid Search Parameter
  • Random Search Parameter Tuning
  • Save and Load (serialize and deserialize) Machine Learning Models
  • Using pickle
  • Using Joblib
  • Finalize a machine learning project
  • Steps For Finalizing classification models - pima indian dataset
  • Dealing with imbalanced class problem
  • Steps For Finalizing multi class models - iris flower dataset
  • Steps For Finalizing regression models - boston housing dataset
  • Predictions and Case Studies
  • Case study 1: predictions using the Pima Indian Diabetes Dataset
  • Case study: Iris Flower Multi Class Dataset
  • Case study 2: the Boston Housing cost Dataset

Machine Learning and Data Science is the most lucrative job in the technology arena now a days. Learning this course will make you equipped to compete in this area.

Best wishes with your learning. Se you soon in the class room.

Worth $ 49.99 $9.99Machine Learning For Data Science Using MATLAB

Basic Course Description 

This course is for you if you want to have a real feel of the Machine Learning techniques without having to learn all the complicated maths. Additionally, this course is also for you if you have had previous hours and hours of machine learning theory but could never got a change or figure out how to implement and solve data science problems with it. 

The approach in this course is very practical and we will start everything from very scratch. We will immediately start coding after a couple of introductory tutorials and we try to keep the theory to bare minimal. All the coding will be done in MATLAB which is one of the fundamental programming languages for engineer and science students and is frequently used by top data science research groups world wide. 

Below is the brief outline of this course. 

Segment 1: Introduction to course

Segment 2: Data preprocessing 

Segment 3: Classification Algorithms in MATLAB

Segment 4: Clustering Algorithms in MATLAB

Segment 5: Dimensionality Reduction

Segment 6: Project: Malware Analysis

Your Benefits and Advantages: 

  • You will be sure of receiving quality contents
  • You have lifetime access to the course.
  • You have instant and free access to any updates i add to the course.
  • You have access to all Questions and discussions initiated by other students.
  • You will receive my support regarding any issues related to the course.
  • Check out the curriculum and Freely available lectures for a quick insight.

It's time to take Action!

Click the "Take This Course" button at the top right now!

Time is limited and Every second of every day is valuable...

We are excited to see you in the course!

Best Regrads,

Dr. Nouman Azam

More Benefits and Advantages: 

✔ You receive knowledge from an experienced instructor (Dr. Nouman Azam) who is the creator of five courses.

✔ The titles of these courses are 

  • Complete MATLAB Tutorial: Go from Beginner to Pro
  • MATLAB App Desigining: The Ultimate Guide for MATLAB Apps
  • Machine Learning Classification Algorithms using MATLAB
  • Create Apps in MATLAB with App Designer (Codes Included)
  • Advance MATLAB Data Types and Data Structures

Student Testimonials for Dr. Nouman Azam!

★★★★★

Already, a couple important concepts have been discussed that weren't discussed in the previous course. I'm glad the instructor is comparing Matlab to Excel, which is the tool I've been using and have been frustrated with. This course is a little more advanced than the previous course I took. As an engineer, I'm delighted it covers complex numbers, derivatives, and integrals. I'm also glad it covers the GUI creation. None of those topics were covered in the more basic introduction I first took.

Jeff Philips

★★★★★ 

Great information and not talking too much, basically he is very concise and so you cover a good amount of content quickly and without getting fed up!

Oamar Kanji

★★★★★

The course is amazing and covers so much. I love the updates. Course delivers more then advertised. Thank you!

Josh Nicassio

Student Testimonials! who are also instructors in the MATLAB category

★★★★★

"Concepts are explained very well, Keep it up Sir...!!!"

Engr Muhammad Absar Ul Haq instructor of course "Matlab keystone skills for Mathematics (Matrices & Arrays)"

Who this course is for:

  • Data Scientists, Researchers, Entrepreneurs, Instructors, College Students, Engineers and Programmers
  • Anyone who want to analyze the data

Worth $ 49.99 $9.99Python Data Science Basics with Numpy, Pandas and Matplotlib

Welcome to my new course Python Essentials with Pandas and Numpy for Data Science

In this course, we will learn the basics of Python Data Structures and the most important Data Science libraries like Numpy and Pandas with step by step examples!

The first session will be a theory session in which, we will have an introduction to python, its applications and the libraries.

In the next session, we will proceed with installing python in your computer. We will install and configure anaconda which is a platform you can use for quick and easy installation of python and its libraries. We will get ourselves familiar with Jupiter notebook, which is the IDE that we are using throughout this course for python coding.

Then we will go ahead with the basic python data types like strings, numbers and its operations. We will deal with different types of ways to assign and access strings, string slicing, replacement, concatenation, formatting and f strings.

Dealing with numbers, we will discuss the assignment, accessing and different operations with integers and floats. The operations include basic ones and also advanced ones like exponents. Also we will check the order of operations, increments and decrements, rounding values and type casting.

Then we will proceed with basic data structures in python like Lists tuples and set. For lists, we will try different assignment, access and slicing options. Along with popular list methods, we will also see list extension, removal, reversing, sorting, min and max, existence check , list looping, slicing, and also inter-conversion of list and strings.

For Tuples also we will do the assignment and access options and the proceed with different options with set in python.

After that, we will deal with python dictionaries. Different assignment and access methods. Value update and delete methods and also looping through the values in the dictionary.

And after learning all of these basic data types and data structures, its time for us to proceed with the popular libraries for data-science in python. We will start with the NumPy library. We will check different ways to create a new NumPy array, reshaping , transforming list to arrays, zero arrays and one arrays, different array operations, array indexing, slicing, copying. we will also deal with creating and reshaping multi dimensional NumPy arrays, array transpose, and statistical operations like mean variance etc using NumPy

Later we will go ahead with the next popular python library called Pandas. At first we will deal with the one dimensional labelled array in pandas called as the series. We will create assign and access the series using different methods.

Then will go ahead with the Pandas Data frames, which is a 2-dimensional labelled data structure with columns of potentially different types. We will convert NumPy arrays and also pandas series to data frames. We will try column wise and row wise access options, dropping rows and columns, getting the summary of data frames with methods like min, max etc. Also we will convert a python dictionary into a pandas data frame. In large datasets, its common to have empty or missing data. We will see how we can manage missing data within dataframes. We will see sorting and indexing operations for data frames.

Most times, external data will be coming in either a CSV file or a JSON file. We will check how we can import CSV and JSON file data as a dataframe so that we can do the operations and later convert this data frame to either CSV and json objects and write it into the respective files. 

Also we will see how we can concatenate, join and merge two pandas data frames. Then we will deal with data stacking and pivoting using the data frame and also to deal with duplicate values within the data-frame and to remove them selectively.

We can group data within a data-frame using group by methods for pandas data frame. We will check the steps we need to follow for grouping. Similarly we can do aggregation of data in the data-frame using different methods available and also using custom functions. We will also see other grouping techniques like Binning and bucketing based on data in the data-frame

At times we may need to use custom indexing for our dataframe. We will see methods to re-index rows and columns of a dataframe and also rename column indexes and rows. We will also check methods to do collective replacement of values in a dataframe and also to find the count of all or unique values in a dataframe.

Then we will proceed with implementing random permutation using both the NumPy and Pandas library and the steps to follow. Since an excelsheet and a dataframe are similar 2d arrays, we will see how we can load values in a dataframe from an excelsheet by parsing it. Then we will do condition based selection of values in a dataframe, also by using lambda functions and also finding rank based on columns.

Then we will go ahead with cross Tabulation of our dataframe using contingency tables. The steps we need to proceed with to create the cross tabulation contingency table.

After all these operations in the data we have, now its time to visualize the data. We will do exercises in which we can generate graphs and plots. We will be using another popular python library called Matplotlib to generate graphs and plots. We will do tweaking of the grpahs and plots by adjusting the plot types, its parameters, labels, titles etc.

Then we will use another visualization option called histogram which can be used to groups numbers into ranges. We will also be trying different options provided by matplotlib library for histogram

Overall this course is a perfect starter pack for your long journey ahead with big data and machine learning. You will also be getting an experience certificate after the completion of the course(only if your learning platform supports)

So lets start with the lessons. See you soon in the class room.

Who this course is for:

  • Data science enthusiasts who want to begin their career

Worth $ 49.99 $9.99Master Clustering Analysis for Data Science using Python

Basic Course Description 

This course is for you if you want to have a real feel of the clustering algorithms without having to learn all the complicated maths. Additionally, this course is also for you if you have had previous hours and hours of classroom theory on the subject but could never got a change or figure out how to implement and solve data science problems with it. 

The approach in this course is very practical and we will start everything from very scratch. We will immediately start coding after a couple of introductory tutorials and we try to keep the theory to bare minimal. All the coding will be done in Python which is one of the fundamental programming languages for engineer and science students and is frequently used by top data science research groups world wide. 

Below is the brief outline of this course. 

  • Segment 1: Introduction to course
  • Segment 2: KMeans Clustering
  • Segment 3: Mean Shift Clustering
  • Segment 4: DBSCAN Clustering
  • Segment 5: Hierarchical Clustering
  • Segment 6: HDBSCAN Clustering
  • Segment 7: Applications of Clustering

Your Benefits and Advantages: 

If you do not find the course useful, you are covered with 20 days money back guarantee, full refund, no questions asked!

  • You will be sure of receiving quality contents since the instructors has already many courses on Data Science on Simpliv
  • You have lifetime access to the course.
  • You have instant and free access to any updates i add to the course.
  • You have access to all Questions and discussions initiated by other students.
  • You will receive my support regarding any issues related to the course.

Check out the curriculum and Freely available lectures for a quick insight.

It's time to take Action!

Click the "Take This Course" button at the top right now!

..Time is limited and Every second of every day is valuable...

We are excited to see you in the course!

Best Regards,

Dr. Nouman Azam

Who this course is for:

  • Data Scientists, Researchers, Entrepreneurs, Instructors, College Students, Engineers and Programmers
  • Anyone who want to analyze the data

Worth $ 49.99 $9.99Intro to Data Science Using Python: Your Best Starting Point

Welcome to “Introduction to Data Science Using Python” where you will set a good foot in the fields of Data Science and Machine Learning.

I'm your instructor Ali Desoki and I start from scratch going clearly over all the points in the course along with hands-on practical exercises and projects to summarize all the skills you’ve learned.

This course is designed for Beginners covering all Aspects of what you need to know to start in the fields of data science and machine learning with practice notebooks which summarize all the skills you’ve learned.

At the end of this course, you will be able to analyze and manipulate data with python and be able to start your career in this field.

This course covers a lot of useful and essential topics including:

  • Introduction to Data Science
  • Data Science Most Used Packages
  • Data Wrangling
  • Model Development
  • Model Refinement
  • Model Evaluation Techniques and more...

The ideal student for this course is someone who looks to start in the mentioned fields from scratch.

All you need to know is Python and basic statistics to start this course.

So what are you waiting for! Enroll now and jump-start your career in Data Science and Machine Learning.

Worth $ 49.99 $9.99

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