Advanced Data Science- Here's How You Can Be a Master the Art of Analytics

Data Science is currently one of the trending fields in tech. However, getting a job in this field depends on proper training and experience.

Named one of the most enticing career options of 21st Century by the Harvard Business Review, the demand for data scientists is increasing at a steady rate. This is the reason that a lot of professionals are now opting for certification in Advanced Data Science. If you are thinking about data science and want to explore it, then a shorter introductory course is more appropriate. However, if you’re going to explore the various intricate details, then an executive certificate in data science would be useful. Data Mining and Data Warehousing ate trending terms in job searches among employers. Having a certificate in data science would help you to reshape your career.

What will you learn during the Advanced Data Science Certification Course?

  • The tailored program designed by leading recruiters

Recruiters have recognized the advanced data science programs as a top-notch one. So, be the data scientist by learning updated technologies and algorithms

  • Gain a foothold in the industry
  • With advanced seminars and hands-on training, you can gain a lot of practical knowledge. You can attend virtual training sessions which are great to shape your fundamentals on data science.

Requirements for Advanced Data Science Certification Course

There are no such elaborate eligibility criteria for attending this workshop. However, you should have a sound knowledge about the basics of Python.

Who can benefit from this course?

This course is specifically designed for individuals who wish to change their domain to Data Science and want to embrace AI revolution. It’s an extensive course with 4-6 industrial projects and with one strategic intent of making candidates ready to deploy.

Course Curriculum

Advance Data Science Course Curriculum

Days: 10
Hours Per Day: 4
Total Hours: 40

1. World of Data Science

(What is Data Science, Why is it important, Life cycle of Data Science, Data Sets, Data Aquisition, Data Pre-Processing, Data Modeling, Data Visualization....)

2. Introduction to Big Data

(What is Big Data, Characteristsics of Big Data - 3Vs, Type of Big Data - Historical and Streaming, Types of Big Data Processing - Batch and Real-time, Facts and Figures)

3. Introduction to AI

(What is AI, Why is it important, Top AI applications, What is ML, What is DL, Co-relation between all 3)

4. Refreshing some Python

(What is Python, Basic Syntax, Python vs C# vs JavaScript, Data Structures, Conditional Programming, Loops)

Labs:

1. Setting up machines with Python

2. Working with data in Python

(Lists, Dictionaries, Tuples, Set)

3. Creating conditional programs in Python

(if-else statements)

4. Creating loops in Python

(For Loop and While Loop)

1. Essential Mathematics for Data Science

(Linear Algebra , Matrix, Probablity, and Calculus) - Real-world Example for every topic

2. Fundamentals of Statistics

(Inferential Statistics and Descriptive Statistics)

3. Introduction to arrays

(What is array, Why do we need them, 1D, 2D, 3d, nD arrays, Python libraries for arrays)

4. Introduction to frames

(What is frame, Why do we need them, Structure of frame, Python libraries for frames)

5. Introduction to Visualization

(Why is visualization important, Charts and Types of charts, Historgrams and Types of histograms, Box Plots, Violin plots, Heat Maps, Scatterplots....)

1. Working with Pandas

(What is Pandas, Advantage of using Pandas, Limitations, Use Cases, Pandas Functions)

2. Working with Numpy

(What is Numpy, What are 1D, 2D and 3D arrrays, Advantage of using Numpy, Limitations, Use Cases, Numpy Functions)

3. Working with Matplotlib

(What is Matplotlib, Advantage of using Matplotlib, Limitations, Use Cases, Matplotlib Functions)

Labs:

1. Data Wrangling with Pandas

(8 functions - Creating frames, Adding rows and collumns, Indexing data...)

2. Creating nD arrays

(6-8 functions - nparray)

3. Data visualization with Matplotlib

(6-8 functions - creating bar chart, creating pi chart, creating box plots, creating scatterplots, creating histograms, boxplots, violin plots....)

1. Types of Machine Learning Models

(What is Supervised, Unsupervised, Reinforcement Learning)

2. Problems solved by Machine Learning

(Classification, Regression, Clustering, Recomendation)

3. Bias, Variance and Standard deviation

(Bias, Variance, Underfitting, Overfitting)

4. Features in Machine Learning

(Feature Selection, Feature Engineering, Feature Extraction, Dimesnionality Reduction)

5. Chi Square Test

(8 functions - Creating frames, Adding rows and collumns, Indexing data...)

6. PCA (Principle Component Analysis)

(6-8 functions - nparray)

7. AutoEncoders

(What are Autoencoders, Types of Autoencoders, Autoencoders vs Other dimensionality reduction methods)

Labs:

1. Performing feature selection and feature engineering

2. Performing dimesnionality reduction with PCA

3. Performing dimesnionality reduction with Auto-encoders

1. Libraries for ML in Python

2. Splitting Datasets

(Training Datasets, Testing Datasets, Evaluation Datasets)

3. Model Parameters

4. Model Hyperparamters

5. Model Evaluation Metrics

(Confusion Matrix, Accuracy, Precision, F1 Score, Logarithmic Loss, Area under curve, Mean Squared Error, Mean absolute error)

6. Ensemble Learning

Labs:

1. Creating a binary classification model to identify the object

2. Evaluating a binary classification model

1. Linear Regression

2. Logistic Regression

3. Decision Trees

4. Random Forest

Labs:

1. Creating a Linear Regression Model to predict sales of vehicles

2. Creating a Logistic Regression Model for Email spam detection

3. Creating a Decision Tree Model for deciding an event

1. K Means Clustering

2. Naive Bayesn

3. Support Vector Machines

Labs:

1. Creating a K-Means clustering Model for grouping datapoints

2. Creating a probabilistic classifier using Naive Bayes

3. Creating a SVM model for performing Regression

1. Introduction to Artificial Neural Networks

2. Understanding Perceptron

3. Weights and Bias

4. Activation Functions

5. Types of Deep Learning Neural Networks

6. Libraries used for Deep Learning

7. Evaluating using K-Fold Cross Validation

8. Applications of DNN

1. Convolutional Neural Network

2. Computer Vision Fundamentals with OpenCV

3. Object Detection algorithms

(R-CNN, Fast R-CNN, Faster R-CNN, SSD, Yolo3, Yolo 4)

4. NLP Fundamentals with SpaCy

Labs:

1. Creating a DNN for Object Detection using Yolo

2. Performing NLP with SpaCy

1. Recurrent Neural Networks

2. LSTM (Long Short Term Memory)

3. Deep Belief Networks

4. GPUs for Deep Learning

5. Cython and CUDA

Labs:

1. Preparing GPU environment for running DNN using N-Series VMs

2. Working with CNN and LSTM model for Speech Synthesis (Tacatron2)

Key Features of Advanced data science Course Training

  • The tailored program designed by leading recruiters
  • Recruiters have recognized the advanced data science programs as a top-notch one. So, be the data scientist by learning updated technologies and algorithms

  • Gain a foothold in the industry
  • With advanced seminars and hands-on training, you can gain a lot of practical knowledge. You can attend virtual training sessions which are great to shape your fundamentals on data science.

Benefits of Advanced data science Program in Data Science Certification Course

Institutional Benefits:

With a Certificate in Advanced Data Science, you would be able to execute data analytics operations, which in turn would be quite beneficial for your employers. Data Scientists in an organization adds value to its culture and working principles with the help of data analysis. Here is the list of institutional benefits an organization can have when they employ a data scientist. 

  • A data scientist can quickly identify the opportunities as well as challenges. With sufficient data mining and data warehousing, trends can be forecasted which would help the organization to align its strategies and tactics
  • Data-driven experience is something which every organization expects to implement in its operation. With a data scientist on board, the organizational decision making would be quantifiable. The stakeholders and customers would have a data-driven experience
  • Helps the organization to identify and refine the target audiences
  • Assists the organization to recruit the right talent for the organization

Career Benefits:

  • Data and information have become critical resources in a wide range of industries. With a certificate in data science, your career path becomes more marketable
  • With a postgraduate degree in data science, your career opportunities are even better. With a master’s degree in data science, you can double up your chances of being hired by reputed organizations
  • With a data science certificate or degree, you would have diverse opportunities. If you are a data specialist, you will be a valuable asset to the organization. It would also help you to earn varied experiences.
  • Helps you to project your role as a leader

FAQs

The training conducted is interactive in nature and easy to learn, focusing on hands-on practical training, use case discussions, and quizzes. In order to improve your online training experience, our trainers use an extensive set of collaborative tools and techniques.

You can attend the training and learn from anywhere in the world through the more preferred, virtual live and interactive training.

It is live and interactive training led by an instructor in a virtual classroom.

If it happens that you miss a class, then you can opt for any of the following two options:

  • Watch the online recording of the session
  • Attend another live batch

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor.

Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% reduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of the written request for a refund. Kindly go through our Refund Policy for more details..

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Average rating:  
 5 reviews
by Ashley on LearNow

The course is extensive, the tools and group they belong to, has been taught in detail. There's a lot of information in there.

by Keith on LearNow

Highly impressed with the rectitude and quality of this course. I am grateful to Vijay for this course and will almost certainly follow it up with more.

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