Fully Immersive Data Scientist2017-11-08T14:00:51+00:00

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Fully Immersive Data Scientist

Fully Immersive Data Scientist

Objective of this Data Scientist Bootcamp

The objective of this data Scientist bootcamp is to provide an introduction to data science theory and practical applications.  The data Scientist bootcamp focuses on fundamental theory. In this data Scientist bootcamp you will receive a full training. This data Scientist bootcamp guarantees you that you will receive all tools end theory needed to become data scientists from experts in the field.

This data Scientist bootcamp takes students through the fundamentals, giving them a solid foundation that they can build upon, then moves on to more advanced knowledge, teaching them how they can apply data science theory in practical situations.

By the end of the course, students will be able to make their own data science models. They will be taught how to create their own models using different languages and different types of methodologies (data science, machine learning, deep learning and so on).

Data science training includes the topics of machine learning, real world recommender system and deep learning, and so on.

An engineer (with data science training) needs to know about all these topics in order to become a data scientist.[/vc_column_text][vc_row_inner][vc_column_inner width=”1/2″][vc_btn title=”ENROL NOW – ONLINE” style=”gradient” gradient_color_1=”mulled-wine” gradient_color_2=”sky” shape=”round” i_align=”right” i_icon_fontawesome=”fa fa-shopping-cart” add_icon=”true” link=”url:https%3A%2F%2Fwww.bigdataguys.com%2Fproduct%2Fdata-science-training-online%2F|||”][/vc_column_inner][vc_column_inner width=”1/2″][vc_btn title=”ENROL NOW – IN CLASS” style=”gradient” gradient_color_1=”mulled-wine” gradient_color_2=”sky” shape=”round” i_align=”right” i_icon_fontawesome=”fa fa-shopping-cart” add_icon=”true” link=”url:https%3A%2F%2Fwww.bigdataguys.com%2Fproduct%2Fdata-science-training-class%2F|||”][/vc_column_inner][/vc_row_inner][/vc_column][vc_column width=”1/3″][vc_row_inner][vc_column_inner css=”.vc_custom_1499974829652{background-image: url(https://www.bigdataguys.com/wp-content/uploads/2017/07/shutterstock_511300690-e1499974695599.png?id=20193) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;}”][vc_custom_heading text=”TRAINING METHODOLOGY” font_container=”tag:h4|font_size:24|text_align:justify|color:%23ffffff” google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:400%20regular%3A400%3Anormal”][vc_separator color=”white” style=”dotted”][vc_column_text]In Class: $9,999
Next Session: 25th Nov 2017[/vc_column_text][/vc_column_inner][/vc_row_inner][vc_row_inner][vc_column_inner css=”.vc_custom_1499974829652{background-image: url(https://www.bigdataguys.com/wp-content/uploads/2017/07/shutterstock_511300690-e1499974695599.png?id=20193) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;}”][vc_column_text]Online: $4,999
Next Session: On Demand[/vc_column_text][/vc_column_inner][/vc_row_inner][vc_row_inner][vc_column_inner][/vc_column_inner][/vc_row_inner][vc_row_inner css=”.vc_custom_1510149566656{background-color: #000000 !important;}”][vc_column_inner][vc_btn title=”Register” style=”gradient” gradient_color_1=”mulled-wine” gradient_color_2=”juicy-pink” shape=”round” align=”center” i_align=”right” i_icon_fontawesome=”fa fa-sign-in” button_block=”true” add_icon=”true” link=”url:https%3A%2F%2Fwww.bigdataguys.com%2Fauth-register%2F|||”][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][/vc_column][/vc_row][vc_row][vc_column width=”2/3″][vc_separator][vc_column_text]Home / All courses / Data Science / Fully Immersive Data Scientist

Fully Immersive Data Scientist

Instructor: John Doe, Lamar George

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Data Scientist

Data Scientist

Data science

Data science is as an evolutionary step in interdisciplinary fields like business analysis that incorporate computer science, modeling, statistics, analytics, and mathematics. Data science uses automated methods to analyze massive amounts of data and to extract knowledge from them. Using these automated methods turning up everywhere. Data science is creating new branches of science, and influencing areas of social science and the humanities. In industry, data science is transforming everything from healthcare to media.  An engineer with data science training needs to know about all these topics in order to become a data scientist.[/vc_column_text][vc_tta_accordion spacing=”2″ active_section=”1″ no_fill=”true”][vc_tta_section title=”Fully Immersive Data Scientist : Machine learning ” tab_id=”1509403004512-1eb9fdd0-d33c”][vc_column_text]Machine learning is popular due to the same factors that have made data mining and Bayesian analysis more popular than ever. Actually, the data volumes are growing and there are more data types available. The computational processing is cheaper and more powerful. And the data storage is affordable.

All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities  (or avoiding unknown risks).

An engineer with data science training needs to know about machine learning in order to become a data scientist.

The types of Machine learning algorithms:

Labeled data: Data consisting of a set of training examples, where each example is a pair consisting of an input and a desired output value

Classification: The goal is to predict discrete values (class)

Regression: The goal is to predict continuous values (numeric).

Types of machine learning Algorithms

There are some variations of how to define the types of Machine Learning Algorithms:

Supervised learning

Unsupervised Learning

Semi-supervised Learning

Reinforcement Learning

Supervised Learning

Supervised learning is when the class or label is known. An algorithm is run and it is used the function that best describes the input data.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Fully Immersive Data Scientist : Machine Learning Algorithms:” tab_id=”1509403004655-0af9e326-8b62″][vc_column_text]Nearest Neighbor

Naive Bayes

Decision Trees

Linear Regression

Support Vector Machines (SVM)

Neural Networks[/vc_column_text][/vc_tta_section][vc_tta_section title=”Fully Immersive Data Scientist : Unsupervised Learning” tab_id=”1509403198872-a29f0a19-0c60″][vc_column_text]Unsupervised learning is when the computer is trained with unlabeled data.

Machine learning algorithms are used to find pattern detection and descriptive modeling. However, there are no output categories or labels here based on which the algorithm can try to model relationships. These algorithms try to use techniques on the input data to mine for rulesdetect patterns, and summarize, which help in deriving meaningful insights and describe the data better to the users.

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Fully Immersive Data Scientist : Semi-supervised Learning” tab_id=”1509403246137-25796db5-a893″][vc_column_text]In semi-supervised learning, either there are no labels for all the observation in the dataset or labels are present for all the observations. Semi-supervised learning falls in between these two. In many practical situations, the cost to label is high. These methods exploit the idea that even though the group memberships of the unlabeled data are unknown, this data carries important information about the group parameters.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Fully Immersive Data Scientist : Reinforcement Learning” tab_id=”1509403286860-b45bb590-9694″][vc_column_text]This method aims at using observations gathered from the interaction with the environment to take actions that would maximize the reward or minimize the risk. Reinforcement learning algorithm (called the agent) continuously learns from the environment in an iterative fashion. In the process, the agent learns from its experiences of the environment until it explores the full range of possible states.[/vc_column_text][/vc_tta_section][vc_tta_section title=”What is Deep Learning?” tab_id=”1509403324815-35ba4a45-00b0″][vc_column_text]Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. An engineer with data science training need to know about deep learning in order to become a data scientist.

Deep learning uses neural networks, which consists of an input layer, hidden layer(s) and an output layers.

Deep learning has many variants and combinations. Furthermore. Many of these networks are combined together in stack or layers to give better results than one network alone.  For example: Multilayer Perceptrons (MLP), Autoencoders (AE), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and so on.

Data Scientist are using data science for decision-making. For example, Data Scientist use data science to solve customer queries based on transaction history. Furthermore, financial and portfolio management institutions deploy sentiment analysis and algorithms to consistently generate handsome returns for customers. Data Scientist are using data science to make it effortlessly achieved. Using quantum advances in technology, Data Science for decision-making is a huge success in the data-driven world.

Data Scientist are helping corporations in sectors such as banking, financial services and insurance, ecommerce, telecom, transportation and so on. These industries and Data Scientist process massive amount of data to leverage their true business potential.

Actually, Data Scientist are having a big success. Analytics revenue is pegged at $2 billion in FY17 whereas startup funding has crossed $700 million in the last two-and-a-half years alone. Data Scientist have an infinite potential.

Actually, companies admitted that they haven’t been able to leverage its benefits due to the shortfall in talent and because of this dearth of highly skilled Data Scientist. Indeed, Data Scientist will command huge salaries. Engineers with data science training are required.

The 80/20 data science dilemma

Most Data Scientist spend only 20% of their time on actual data analysis and 80% of their time finding, cleaning, and reorganizing huge amounts of data, which is an inefficient data strategy.

The Cloud technology has led to an explosion of data that has left Data Scientist in high demand.

Though growing in population, Data Scientist are so busy. The demand for Data Scientist and analysts is projected to grow by 28% by 2020. The demand for Data Scientist is on top of the current market need. According to LinkedIn, there are more than 11,000 data scientists job opening in the US.

Many Data Scientist work for companies.

The reason Data Scientist are hired in the first place is to develop algorithms and build machine-learning models. In most companies today, 80% of a data scientist’s valuable time is spent simply finding, cleaning and reorganizing huge amounts of data.

Hard work behind the scenes of Data Scientist

Data Scientist must identify relevant data sets within their data storage repositories (data lakes), which is a huge task.

A disadvantage of these data lakes is that data lakes have turned into dumping grounds, and Data Scientist don’t have an easy way to search for data and unclear strategies and policies around what data is safe to share more broadly. Data Scientist often find themselves contacting different departments for the data they need and too much for this data to be delivered, and sometimes this data doesn’t provide the information that Data Scientist need, and sometimes data has serious quality issues. At the same time, responsibility for data governance (or data-sharing policies) often falls on Data Scientist, since corporate-level governance policies can often be confusing, inconsistent, or difficult to enforce.

Even when Data Scientist can get the right data, Data Scientist need to time to explore and understand it. The data may be in a format that can’t be easily analyzed, and with little to no metadata to help, the Data Scientist may need to seek advice from the data owner. After all this, Data Scientist need to prepare the data for analysis. Data Scientist have different tasks such as formatting, cleaning and sampling the data. Data Scientist do other tasks such as scaling, decomposition and aggregation transformations, which are required before Data Scientist are ready to start training the models.

Data Scientist and developers traditionally work alone. This creates bottlenecks, increases the errors and dries up resources. Data Scientist and developers working together could use cloud platforms to be more efficient and to help Data Scientist to work together.

These processes can be time-consuming and tedious, but they are very important. Data Scientist can find models that generally improve as they are exposed to increasing amounts of data. Data Scientist have to decide to include as much data as they can in their analysis in order to have better models.

Data Scientist when don’t have enough time, they are tempted to make compromises on the used data, aiming for “good enough” rather than optimal results.

Data Scientist can make hasty decisions during model development that can lead to widely different outputs and potentially render a model unusable for production Engineers (with data science training.) They are constantly making judgment calls, and starting out with incomplete data can easily lead them down the wrong path.

Data Scientist are generally forced to focus on one model at a time in order to balance quality against time constraints. Data Scientist are forced to start all over again when something goes wrong. Data Scientist are obliged to double down on every hand, turning data science into a high-stakes game of chance.

Data Scientist use cloud data services to automate many of the tedious processes associated with finding. Data Scientist clean data to have more time for analysis, without compromising the quality of the data they use.

A solid cloud data platform is used to help Data Scientist find the data they need. Integrated data governance tools also give Data Scientist confidence that they are permitted to use a given data set.

As a result, Data Scientist gain the time they need to build and train multiple models simultaneously. This is a great advantage of parallel experimentation that yields breakthroughs without focusing resources on a single approach that may turn out to be a dead end.

Data Scientist use cloud platforms to save, access and extend models, enabling them to use existing assets as templates for new projects instead of starting from scratch every time, which is called “transfer learning”. Preserving knowledge and apply model in related problems is used by Data Scientist. Data Scientist use these models for saving time and reducing risk.

Data Scientist play an essential role in pushing forward innovation and garnering competitive advantage for companies Data Scientist use cloud platform tools to have better results in less time.

The Data Science Product Manager – New Roles For The New Business Reality

Data Scientist have a great opportunity because new markets are emerging around data science. Data Science Product Manager is the most important role, which is the link between research and ROI. Facebook companies have startups that have heavy emphasis on results (productization) for their data science efforts. This company wants Data Scientist to work.

Data Scientist and machine learning engineers are very promising.

Dataiku and data science market

The company Dataiku Inc., which manufactures Dataiku Data Science Studio (DSS), providing end-to-end advanced analytics and collaborative data science tools, has announced plans to strengthening its platform with new technologies and more Data Scientist.

Battery Ventures, FirstMark, Serena Capital and Alven are investing $28 million. This level of investment is reflective of an increased demand by firms, across a broad range of industries, to maximize their data production and analysis. The Dataiku platform allows Data Scientist, programmers, and analysts to collaborate more effectively both within and across organizations.

Many businesses are investing in data science and data Scientist bootcamps. Actually, big data is increasing and methods for data collection are improved. Data science is about leveraging the information that has been collected and using analytics to use this data in more effective ways. The process is multifaceted and it includes technology, mathematics, and human insight.

Dataiku was founded in 2014, and it offers collaborative data science systems to global companies. Dataiku is a company for selecting the right data science tool for companies such as L’Oreal, NPR, AXA, and Kuka.

In these types of businesses, Data Scientist are using data to find and interpret rich data sources. Data Scientist have task such as managing of large amounts of data, merging data sources, ensuring consistency of datasets, creating visualizations to help interpret data, building mathematical models, and communicating out data insights.

Battery Ventures said data science has become mission-critical to all types of businesses. The problem is that there are still a limited number of people with true technical, data science capabilities.

Data Scientist are using Dataiku software to manipulate the dataset, which is a series of records with the same schema. Dataiku will hire 100 new Data Scientist and accelerate the development of new features within their platform. The company aims to further drive through changes as it positions itself as a disruptive force in data analytics.

IBM’s Watson Data Platform aims to become data science operating system

IBM’s plan is to create a data science operating system that can bring together Data Scientist, analysts, and business leaders.

IBM is aiming to make Watson Data Platform a system for data science in the months ahead as it compiles various functions and parts and combines them into a cloud-based effort to make data more consumable.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Turning big data into business insight through 2017″ tab_id=”1509403522114-226713c1-348b”][vc_column_text]Data Scientist use Watson Data Platform, which is a collection of services used to prepare, store, ingest, and analyze data and then allow customers to build applications on top of it.

Watson’s potential as a data operating system is highlighted in IBM’s Data Science Experience. Data Scientist use this platform, which is a collection of tools designed to provide insights.

Data Scientist use Watson Data Platform, which has been adding new functionality around cataloguing data. Actually, IBM can move data into Watson Data Platform and make it consumable the faster it can be a data science operating system.

IBM wants to be the company that best helps businesses benefit from their data.

Data science today is an aggregation of parts and roles and piecemeal applications.

Data Scientist use data science and a combination of disciplines ranging from business intelligence and analysis to data science to mathematics and statistics.

Simplifying the moving technology parts of data science will enable business leaders to more easily transform their businesses.

IBM wants to make Watson Data Platform a dynamic catalog that can be modeled and shared across all teams and functions. IBM created a data science operating system and wants to play a big role in artificial intelligence.

IBM has a cloud model that allows customers to buy functions and specific services. IBM customers already have applications such as Cloudant, SPSS, and Cognos. Packaging, pricing, and look and feel of the Watson Data Platform will be critical. Data Scientist use these applications.

IBM has all of the key parts. Actually, IBM is a leader in data science platforms and noted strengths included a strong customer base, commitment to open source technologies, strong model management, and governance and support for a broad range of data types. Data Scientist uses IBM software.

IBM’s data science platform hurdles include overcoming confusion about its product offerings, confusing roadmaps, and customer support. Actually, IBM’s Data Science Experience (DSx) could address shortcomings.

Gartner Data Science Platform and IBM have as a good chance to form a data science operating system as anyone. Data Scientist can use this platform.

Data science is a booming academic discipline, which is becoming big business. Data Scientist bootcamp are becoming a big business.  It involves the collecting, organizing and interpreting of large sets of digital information and has the potential to transform public and private organizations.

The data science technologies and services behind it could be worth $2.7 trillion globally by 2020, according to the International Data Corporation. It is a potential driver of economic growth as a new cluster of companies emerge to exploit its potential to help firms improve their operations, understand how their own technology works and provide early warning of imminent problems.

EDF Energy was an early adopter of Data Science technologies. SSE is using it to predict faults, and thereby get its repair teams more quickly to locations where they know problems are likely to occur.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Solving the ‘Last Mile’ Problem in Data Science” tab_id=”1509403634026-9c5d3c02-d053″][vc_column_text]Machine learning technologies and new techniques are creating new market for data science. It is a huge opportunity to become data scientist. A company called Open Data Group is aiming to close that gap with a Docker-based model deployment framework.

Open Data Group’s CTO Stu Baily describes the company’s FastScore framework as an abstraction layer that makes it easier for enterprise IT professionals to deploy data science models into production environments. Engineers with data science training use this framework.

This company is focused on bridging analytic professionals, Engineers with data science training, quants, model builders, analytic engineers. Their solution focuses on getting models deployed as durable, cloud portable assets that will have a very long time, but can be easily changed, easily migrated. This solution is used by engineers with data science training.

FastScore can use any language or environments the analytic model is developed in. FastScore supports models developed in Python, R, SAS, H2O.ai, and Juypter and Apache Zeppelin data science notebook, among others. Once the model is created and logged in its Avro schema, FastScore can transform it into a Docker microservice that can be called with a REST API, which IT professionals are familiar with. Engineers with data science training can use any language with this framework.

The aim of this framework is the integration of model building tools, but their focus is building a very clear abstraction for handing off models from the data lab or the data science process into pre-production and production environments. Engineers with data science training can use any model with this framework.

The company is just as neutral when it comes to production environments as it is for data science development environments. Users can use whatever scheduling system they want, including Kubernetes, DC/OS, or CloudFoundry, while it supports data stores like S3, HDFS, and relational databases. Engineers with data science training can use this framework with any scheduling system.

Open Data Group wants to have their engineers with data science training together and that they get involve with the day-to-day management of machine learning models or data science models.

Open Data Group aims to streamline production deployment of analytic models with its FastScore framework

Open Data Group recently dealt with a large manufacturer that was struggling to get machine learning models (or data science models) into production.

Open Data Group also tends to the daily care and feeding of the models, which it refers to as AnalyticsOps. Their engineers with data science training offers hooks into code repositories like GitHub, model management functionality, and AB testing capabilities for comparing the effectiveness of models.

Their engineers with data science training have a simple set of abstractions, a very consumable technology stack that really makes the data science much more productive, but it’s built like IT would expect it to be built, in a modular way that’s future proof for their own journey. And then if engineers with data science training want to move their models to Google, then it would be easy. And engineers with data science training should have a very high degree of confidence that all the math is absolutely going to be the same.”

Open Data Group has been in the machine learning and applied statistics business for 18 years, and has quite a bit of experience in helping customers get real benefits out of their data.

Open Data Group’s CEO said that the timing is right for companies to get more serious about data science and how they and their engineers with data science training can use it to improve their business.

Actually, there is a transformation of large portions of the economy and industrial sector with data science in a somewhat analogous way to how computer science really started to have a huge impact in the 80s and 90s.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Data Scientist Bootcamp” tab_id=”1509403688250-e01fad10-9a44″][vc_column_text]Bigdataguys has organized courses to help developers (or any person that wants to become data scientist) gain a greater understanding of data science. This course gives you excellent opportunities in the job market. These classes aim to bring students up to speed on data science, as well as give them practical skills that will help their careers about data science, machine learning, deep learning, and so on.

Data Scientist bootcamp of Bigdaaguys offers qualify courses in data science to become data scientist. The best way to learn about data science is to take a course with us. Data science training covers the basic theory and practical examples.

The typical Google Data Scientist salary is $152,417. Data Scientist salaries at Google can range from $103,000 – $195,000.[/vc_column_text][/vc_tta_section][/vc_tta_accordion][vc_separator color=”custom” style=”shadow” accent_color=”#0a57e8″][vc_separator color=”custom” style=”shadow” accent_color=”#0a57e8″][vc_custom_heading text=”CURRICULUM ” font_container=”tag:h4|text_align:center”][vc_tta_accordion spacing=”2″ active_section=”1″ no_fill=”true”][vc_tta_section title=”WEEK 1-GOOD ENGINEERING PRACTICES IN PYTHON, CREATING AND CONSUMING API’S” tab_id=”1509403781898-c27c5de6-ffa1″][vc_column_text]Lecture1.1 Introduction to Python
Lecture1.2 Complete Syntax
Lecture1.3 Data Structures
Lecture1.4 Object oriented programming and introduction to the functional paradigm
Lecture1.5 Python idioms and things to look out for Tooling
Lecture1.6 How to use a Python IDE and text editors effectively
Lecture1.7 Basic command line tricks
Lecture1.8 Learn how to use git and some git workflows
Lecture1.9 Learn the ropes of the Python ecosystem
Lecture1.10 Virtual environments for package isolation
Lecture1.11 Build distribution packages and learn about different ways of distributing code
Lecture1.12 Writing tests
Lecture1.13 Web APIs
Lecture1.14 Design an API considering use cases in advance
Lecture1.15 Write a simple API and produce self-documentation for it
Lecture1.16 Write tests for APIs[/vc_column_text][/vc_tta_section][vc_tta_section title=”WEEK 2- MACHINE LEARNING OVERVIEW: PROFICIENCY WITH CORE METHODS” tab_id=”1509403782069-a5f780b4-4f85″][vc_column_text]Lecture2.1 Regression: cost functions (average squared error and friends; when to pick a non-standard cost function)
Lecture2.2 Random forests
Lecture2.3 Ensembles
Lecture2.4 SVMs
Lecture2.5 Basic idea
Lecture2.6 Kernels: understanding the basic types
Lecture2.7 Model comparison
Lecture2.8 Categorization: ROC curves
Lecture2.9 Common misconceptions, Common ways to optimize them, Best Use Cases[/vc_column_text][/vc_tta_section][vc_tta_section title=”WEEK 3- ADVANCED MACHINE LEARNING: MODEL PIPELINES” tab_id=”1509404033546-1ed0893c-f4fc”][vc_column_text]Once your company starts fitting models, methodology matters. It is easy to simply pile up complexity without managing it. Fortunately, we now have best practices (and libraries) that make it easy to iterate over preprocessing, model families, and parameters.
Lecture3.1 How to weight, transform, combine, or drop features
Lecture3.2 How to represent transformations, models, parameters, and the results of a run, so they can be easily managed
Lecture3.3 What feature transformations add the most performance, and how they interact with the rest of the pipeline[/vc_column_text][/vc_tta_section][vc_tta_section title=”WEEK 4- NUMPY, SPICY, PANDAS, AND SCIKIT- LEARN” tab_id=”1509404070118-44c86b6b-d42c”][vc_column_text]Lecture4.1 Preprocessing with Pandas
Lecture4.2 Reading data
Lecture4.3 Selecting columns and rows
Lecture4.4 Filtering
Lecture4.5 Vectorized string operations
Lecture4.6 Missing values
Lecture4.7 Handling time
Lecture4.8 Time series
Lecture4.9 Numpy, Spicy
Lecture4.10 Arrays
Lecture4.11 Indexing, Slicing and Iterating
Lecture4.12 Reshaping
Lecture4.13 Shallow vs deep copy
Lecture4.14 Broadcasting
Lecture4.15 Indexing (advanced)
Lecture4.16 Matrices
Lecture4.17 Matrix decompositions
Lecture4.18 Scikit-learn
Lecture4.19 Feature extraction
Lecture4.20 Classification
Lecture4.21 Regression
Lecture4.22 Clustering
Lecture4.23 Dimension reduction
Lecture4.24 Model selection[/vc_column_text][/vc_tta_section][vc_tta_section title=”WEEK 5-REAL TIME STREAM PROCESSING WITH SPARK, KAFKA, AND ELASTIC SEARCH” tab_id=”1509404120601-e993194d-6031″][vc_column_text]Lecture5.1 Kafka
Lecture5.2 Setup and configuration
Lecture5.3 Topics, partitions
Lecture5.4 API
Lecture5.5 Connecting to Spark
Lecture5.6 Elasticsearch
Lecture5.7 Setup
Lecture5.8 API
Lecture5.9 Kibana
Lecture5.10 Marvel Plugin
Lecture5.11 Real-time Data Pipeline
Lecture5.12 Twitter API
Lecture5.13 Spark streaming[/vc_column_text][/vc_tta_section][vc_tta_section title=”WEEK 6-REAL WORLD RECOMMENDER SYSTEM” tab_id=”1509404170729-8ce41179-5a6e”][vc_column_text]Recommendations are often used in many industries, such as e-commerce, jobs, music, and social media. This course goes beyond the basics and emphasizes solutions to problems you will face when your business deploys a recommender system.
Lecture6.1 How recommenders work, using both content-based and collaborative filtering techniques.
Lecture6.2 How to build recommenders that scale. On platforms where both the number of users and/or items (such as movies, or products, or job openings) may be very large — i.e. in the millions — thinking about scaling is essential.
Lecture6.3 How to factor in business concerns: e.g. pricing, inventory, seasonality, new items, new users, popular products, serendipity, coverage, etc.
Lecture6.4 How to tune and evaluate a recommender.
Lecture6.5 How to generate recommendations in real-time.[/vc_column_text][/vc_tta_section][vc_tta_section title=”WEEK 7- DEEP DIVE INTO R” tab_id=”1509404218354-22378cea-95a8″][vc_column_text]Lecture7.1 R basic data types
Lecture7.2 Atomic vectors and operations on them
Lecture7.3 Lists
Lecture7.4 R as a functional programming language
Lecture7.5 Object attributes and object oriented programming
Lecture7.6 R complex data types (matrices, factors, and data frames)
Lecture7.7 Unit testing
Lecture7.8 Debugging, and code profiling
Lecture7.9 Writing R packages
Lecture7.10 String processing, dates, regular expressions (using the stringi package)
Lecture7.11 Speeding up operations on multiple files
Lecture7.12 Dynamic report generation with knitr[/vc_column_text][/vc_tta_section][vc_tta_section title=”WEEK 8- SPEEDING UP R AND PYTHON MODELS: RCPP AND CYTHON” tab_id=”1509404295904-fdf70b9e-a1a0″][vc_column_text]Lecture8.1 What are Rcpp and Cython. Why C++ for data science?
Lecture8.2 C++ introduction: scalar data types, controlling program flow
Lecture8.3 Accessing R vectors through Rcpp
Lecture8.4 Lists and R functions
Lecture8.5 C++ Standard Library – fundamental data structures and algorithms
Lecture8.6 Introduction to Cython, linking C++ libraries to Cython; accessing NumPy objects
Lecture8.7 OpenMP – multi threaded C++ made simple[/vc_column_text][/vc_tta_section][vc_tta_section title=”WEEK 9-OPTIMIZING DATA STRUCTURES AND MEMORY USAGE: ADVANCED DATA TABLE” tab_id=”1509404351510-a78ba7f6-fa26″][vc_column_text]Lecture9.1 Aggregations (split-apply-combine type operations)
Lecture9.2 Add/update/delete columns without any unnecessary copies (by reference)
Lecture9.3 File reader (fread)
Lecture9.4 Ordered and rolling joints
Lecture9.5 Overlapping range/interval joins
Lecture9.6 Reshaping etc.[/vc_column_text][/vc_tta_section][vc_tta_section title=”WEEK 10-DEEP LEARNING FOR IMAGE CLASSIFICATION” tab_id=”1509404395124-4b1e3bdf-d93e”][vc_column_text]Lecture10.1 Background on neural nets, history, performance bottlenecks
Lecture10.2 Training deep nets
Lecture10.3 Regularization (dropout)
Lecture10.4 Interpreting weights on a hidden layer[/vc_column_text][/vc_tta_section][/vc_tta_accordion][vc_row_inner][vc_column_inner width=”1/2″][vc_btn title=”ENROL NOW – ONLINE” style=”gradient” gradient_color_1=”mulled-wine” gradient_color_2=”sky” shape=”round” i_align=”right” i_icon_fontawesome=”fa fa-shopping-cart” add_icon=”true” link=”url:https%3A%2F%2Fwww.bigdataguys.com%2Fproduct%2Fdata-science-training-online%2F|||”][/vc_column_inner][vc_column_inner width=”1/2″][vc_btn title=”ENROL NOW – IN CLASS” style=”gradient” gradient_color_1=”mulled-wine” gradient_color_2=”sky” shape=”round” i_align=”right” i_icon_fontawesome=”fa fa-shopping-cart” add_icon=”true” link=”url:https%3A%2F%2Fwww.bigdataguys.com%2Fproduct%2Fdata-science-training-class%2F|||”][/vc_column_inner][/vc_row_inner][vc_row_inner css=”.vc_custom_1500072288565{margin-right: 10px !important;background-image: url(https://www.bigdataguys.com/wp-content/uploads/2017/07/shutterstock_511300690-e1499974695599.png?id=20193) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;}”][vc_column_inner width=”2/3″][vc_column_text]Online: $4,999
Next Batch: On Demand[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/3″][vc_btn title=”ENROL NOW” style=”outline-custom” outline_custom_color=”#ffffff” outline_custom_hover_background=”#029dff” outline_custom_hover_text=”#ffffff” shape=”round” size=”lg” align=”right” i_align=”right” i_icon_fontawesome=”fa fa-shopping-cart” add_icon=”true” link=”url:https%3A%2F%2Fwww.bigdataguys.com%2Fproduct%2Fdata-science-training-online%2F||target:%20_blank|”][/vc_column_inner][/vc_row_inner][vc_row_inner css=”.vc_custom_1500072200986{margin-right: 10px !important;background-image: url(https://www.bigdataguys.com/wp-content/uploads/2017/07/bh.png?id=20198) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;}”][vc_column_inner width=”2/3″][vc_column_text]In Class: $9,999
Locations: New York City, D.C., Bay Area
Next Batch: starts from 25th Nov 2017
[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/3″][vc_btn title=”ENROL NOW” style=”outline-custom” outline_custom_color=”#ffffff” outline_custom_hover_background=”#029dff” outline_custom_hover_text=”#ffffff” shape=”round” size=”lg” align=”right” i_align=”right” i_icon_fontawesome=”fa fa-shopping-cart” add_icon=”true” link=”url:https%3A%2F%2Fwww.bigdataguys.com%2Fproduct%2Fdata-science-training-class%2F||target:%20_blank|”][/vc_column_inner][/vc_row_inner][/vc_column][vc_column width=”1/3″ css=”.vc_custom_1499982058886{background-image: url(https://www.bigdataguys.com/wp-content/uploads/2017/07/shutterstock_511300690-1.png?id=20194) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;}”][vc_custom_heading text=”COURSE HIGHLIGHTS” font_container=”tag:h4|font_size:24|text_align:justify|color:%23ffffff” google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:400%20regular%3A400%3Anormal”][vc_separator color=”white” style=”dotted”][vc_column_text]Skill level: Intermediate
Language: English
Certificate: No
Assessments: Self
Prerequisites: Basic Python programming[/vc_column_text][vc_row_inner css=”.vc_custom_1500308583613{background-color: #ffffff !important;}”][vc_column_inner css=”.vc_custom_1500308283314{border-radius: 1px !important;}”][/vc_column_inner][/vc_row_inner][vc_custom_heading text=”SCHEDULE YOUR FREE DEMO” font_container=”tag:h4|font_size:24|text_align:center|color:%23ffffff” google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:400%20regular%3A400%3Anormal” link=”url:https%3A%2F%2Fcalendly.com%2Fbigdataguysadmin%2F15min||target:%20_blank|”][vc_btn title=”click to schedule” style=”gradient-custom” gradient_custom_color_1=”#0a57e8″ gradient_custom_color_2=”#029dff” shape=”round” align=”center” i_icon_fontawesome=”fa fa-calendar” button_block=”true” add_icon=”true” link=”url:https%3A%2F%2Fcalendly.com%2Fbigdataguysadmin%2F15min||target:%20_blank|”][vc_row_inner css=”.vc_custom_1500308583613{background-color: #ffffff !important;}”][vc_column_inner css=”.vc_custom_1500308283314{border-radius: 1px !important;}”][/vc_column_inner][/vc_row_inner][vc_custom_heading text=”TALK TO US ” font_container=”tag:h4|font_size:24|text_align:center|color:%23ffffff” google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:400%20regular%3A400%3Anormal”][vc_btn title=”+1 202-897-1944″ style=”gradient-custom” gradient_custom_color_1=”#0a57e8″ gradient_custom_color_2=”#029dff” shape=”round” align=”center” i_icon_fontawesome=”fa fa-volume-control-phone” button_block=”true” add_icon=”true”][vc_row_inner css=”.vc_custom_1500308583613{background-color: #ffffff !important;}”][vc_column_inner css=”.vc_custom_1500308283314{border-radius: 1px !important;}”][/vc_column_inner][/vc_row_inner][vc_custom_heading text=”NEED CUSTOM TRAINING FOR YOUR CORPORATE TEAM?” font_container=”tag:h4|font_size:20|text_align:center|color:%23ffffff” google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:400%20regular%3A400%3Anormal” link=”url:https%3A%2F%2Fwww.bigdataguys.com%2Fcontactus%2F||target:%20_blank|”][vc_btn title=”GET QUOTE” style=”gradient-custom” gradient_custom_color_1=”#0a57e8″ gradient_custom_color_2=”#029dff” shape=”round” align=”center” button_block=”true” link=”url:https%3A%2F%2Fwww.bigdataguys.com%2Fcontactus%2F||target:%20_blank|”][vc_row_inner css=”.vc_custom_1500308583613{background-color: #ffffff !important;}”][vc_column_inner css=”.vc_custom_1500308283314{border-radius: 1px !important;}”][/vc_column_inner][/vc_row_inner][vc_custom_heading text=”NEED HELP? MESSAGE US” font_container=”tag:h4|font_size:24|text_align:center|color:%23ffffff” google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:400%20regular%3A400%3Anormal”][vc_separator color=”white” style=”dotted”][vc_column_text]

[/vc_column_text][vc_row_inner css=”.vc_custom_1500308583613{background-color: #ffffff !important;}”][vc_column_inner css=”.vc_custom_1500308283314{border-radius: 1px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner][vc_column_inner][vc_custom_heading text=”SOME COURSES YOU MAY LIKE” font_container=”tag:h4|font_size:24px|text_align:justify|color:%23ffffff” google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:400%20regular%3A400%3Anormal”][vc_separator color=”white” style=”dashed”][/vc_column_inner][/vc_row_inner][vc_tta_pageable no_fill_content_area=”1″ autoplay=”3″ active_section=”1″ pagination_color=”white”][vc_tta_section title=”Section 1″ tab_id=”1500067469553-ae818e0b-9db2″][vc_single_image image=”19441″ img_size=”full” alignment=”center”][vc_column_text]

Deep Learning with Tensor Flow In-Class or Online

Good grounding in basic machine learning. Programming skills in any language (ideally Python/R).

Instructors: John Doe, Lamar George
50 hours
Lectures:  25

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Section 2″ tab_id=”1500067469854-6c4f938f-a6a8″][vc_single_image image=”19327″ img_size=”full” alignment=”center”][vc_column_text]

Neural Networks Fundamentals using Tensor Flow as Example Training (In-Class or Online) 

Good grounding in basic machine learning. Programming skills in any language (ideally Python/R).

Instructors: John Doe, Lamar George
50 hours
Lectures:  25

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Section” tab_id=”1500067796681-c32c5033-4285″][vc_single_image image=”19051″ img_size=”full” alignment=”center”][vc_column_text]

Tensor Flow for Image Recognition Bootcamp (In-Class and Online)

Good grounding in basic machine learning. Programming skills in any language (ideally Python/R).

Instructors: John Doe, Lamar George
50 hours
Lectures:  25

[/vc_column_text][/vc_tta_section][/vc_tta_pageable][vc_row_inner css=”.vc_custom_1500308583613{background-color: #ffffff !important;}”][vc_column_inner css=”.vc_custom_1500308283314{border-radius: 1px !important;}”][/vc_column_inner][/vc_row_inner][vc_custom_heading text=”OUR PRODUCTS” font_container=”tag:h4|font_size:24px|text_align:justify|color:%23ffffff” google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:400%20regular%3A400%3Anormal”][vc_separator color=”white” style=”dashed”][vc_column_text]VIDEOS
SECURITY[/vc_column_text][vc_row_inner css=”.vc_custom_1500308583613{background-color: #ffffff !important;}”][vc_column_inner css=”.vc_custom_1500308283314{border-radius: 1px !important;}”][/vc_column_inner][/vc_row_inner][vc_custom_heading text=”SOME OTHER COURSES YOU MAY LIKE” font_container=”tag:h4|font_size:24px|text_align:left|color:%23ffffff” google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:400%20regular%3A400%3Anormal”][vc_separator color=”white” style=”dashed”][vc_column_text]Workday Financials
Machine Learning AI
Machine Learning With Tenser Flow
Hadoop Training
The Complete Bitcoin Course: Get .001 Bitcoin In Your Wallet
Intro to Dash Crypto Currency[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column width=”1/2″][vc_text_separator title=”FAQ’S” color=”custom” accent_color=”#029dff”][vc_tta_accordion spacing=”2″ active_section=”1″ no_fill=”true”][vc_tta_section title=”What is the duration of the course?” tab_id=”1509404795084-d1fd7ffe-6a6a”][vc_column_text]Advanced Course like Fully Immersive Data Scientist  duration largely depends on trainee requirements, it is always recommended to consult one of our advisors for specific course duration.[/vc_column_text][/vc_tta_section][vc_tta_section title=”What If I Miss A Class?” tab_id=”1509404795266-e3c48024-e450″][vc_column_text]We record each LIVE class session you undergo through and we will share the recordings of each session/class.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Can I Request For A Support Session If I Find Difficulty In Grasping Topics?” tab_id=”1509964335705-11cfadfb-c7e0″][vc_column_text]If you have any queries you can contact our 24/7 dedicated support to raise a ticket. We provide you email support and solution to your queries. If the query is not resolved by email we can arrange for a one-on-one session with our trainers.[/vc_column_text][/vc_tta_section][vc_tta_section title=”What Kind Of Projects Will I Be Working On As Part Of The Training?” tab_id=”1509964370935-212b685d-6ade”][vc_column_text]You will work on real world projects wherein you can apply your knowledge and skills that you acquired through our training. We have multiple projects that thoroughly test your skills and knowledge of various aspect and components making you perfectly industry-ready.[/vc_column_text][/vc_tta_section][vc_tta_section title=”How Will I Execute The Practical?” tab_id=”1509964421229-b67ed16e-3909″][vc_column_text]Our Trainers will provide the Environment/Server Access to the students and we ensure practical real-time experience and training by providing all the utilities required for the in-depth understanding of the course.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Are These Classes Conducted Via Live Online Streaming?” tab_id=”1509964460052-b7e2cbff-d85d”][vc_column_text]Yes. All the training sessions are LIVE Online Streaming using either through WebEx or GoToMeeting, thus promoting one-on-one trainer student Interaction.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Will the course fetch me a job?” tab_id=”1509964494633-381bda95-852d”][vc_column_text]The Fully Immersive Data Scientist by BigdataGuys will not only increase your CV potential but will offer you a global exposure with enormous growth potential.[/vc_column_text][/vc_tta_section][/vc_tta_accordion][/vc_column][vc_column width=”1/2″][vc_text_separator title=”REVIEWS” color=”custom” style=”dashed” accent_color=”#0a57e8″][vc_column_text][rwp_box_recap id=”0″][/vc_column_text][vc_separator][vc_text_separator title=”COMMENTS” color=”custom” style=”dashed” accent_color=”#0a57e8″][vc_wp_recentcomments number=”3″][/vc_column][/vc_row][vc_row][vc_column][vc_custom_heading text=”BLOG” font_container=”tag:h4|font_size:24|text_align:center|color:%23000000″ google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:700%20bold%20regular%3A700%3Anormal”][vc_masonry_grid post_type=”post” max_items=”3″ grid_id=”vc_gid:1510149502482-7f62f9c2-8213-10″][vc_separator color=”custom” accent_color=”#029dff”][/vc_column][/vc_row][vc_row][vc_column][vc_custom_heading text=”INSTRUCTORS” font_container=”tag:h4|font_size:24px|text_align:center|color:%23029dff” google_fonts=”font_family:Open%20Sans%3A300%2C300italic%2Cregular%2Citalic%2C600%2C600italic%2C700%2C700italic%2C800%2C800italic|font_style:400%20regular%3A400%3Anormal”][vc_row_inner css=”.vc_custom_1500073289059{border-radius: 1px !important;}”][vc_column_inner width=”1/2″ css=”.vc_custom_1500073307975{border-radius: 1px !important;}”][vc_column_text]

John Doe
Learning Scientist & Master Trainer 
John Doe has been a professional educator
for the past 20 years. He’s taught, tutored,
and coached over 1000 students, and he
holds degrees in Physics and Literature
from Northwestern University. He has
spent the last 4 years studying how
people learn to code and develop applications.

[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/2″][vc_column_text]

Lamar George
Learning Scientist & Master Trainer 
He has been a professional educator for
the past 20 years. He’s taught, tutored,
and coached over 1000 students, and
he holds degrees in Physics and Literature
from Northwestern University. He has
spentthe last 4 years studying how
people learn to code and develop applications.