Data Science

Unlock the Data – The world of Data Science Data Science is the art of generating insight, knowledge and predictions by processing of data gathered about a system or a process. Computational Science is the art of developing validated (simulation) models in order to gain a better understanding of a phenomenon (system’s or processes). Computational sciences focus on development of causal models using latent patterns in the observed data, rather than only extracting patterns or knowledge from data by statistical methods. Data is the new oil and Data Science is its combustion engine! While there are many definitions as to what data science really is, we have found it best to describe it as a field revolving around 5 data-related operations. Collection | Storing | Processing |Describing | Modeling

What you'll do

Learn how to source, manipulate and visualize data using Python and its libraries. Build and refine your Machine Learning Skills with the help of topics like Statistics, Trees, Neutral Networks etc. To produce professionals with deep knowledge and innovative analytical and computational research skills to handle problems in a variety of domains including governance, finance, security, transportation,healthcare, energy management, agriculture, population studies, weather prediction, economics, social sciences, predictive maintenance, structural health monitoring, smart manufacturing and computational structural biology.

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From $149
16-06-2021 $149

What You Will Learn

Data is the new oil and Data Science is its combustion engine! While there are many definitions as to
what data science really is, we have found it best to describe it as a field revolving around 5 data-related
Collection | Storing | Processing |Describing | Modeling

 Collection
Data Collection is the process of gathering data (Numerical, text, video, audio etc), influenced by two
major factors namely, the question that needs to be answered by the data scientist and the
environment that the data scientist is working in!
 Storing
Storing data involves maintaining the collected data for use during the data science pipeline. Structured
data is typically stored in relational-databases and aggregated in data-warehouses. With the advent of
Big-Data, Data Lakes are now used to store multimodal structured and unstructured data.
 Processing
Data Processing is a set of 3 main sub-processes. Data Wrangling (Extraction, transformation, and
loading of the data), Data Cleaning (Handling Missing Values, Outliers, etc) and Data Scaling,
Normalization and Standardization.
 Describing
Data Description has two aspects. Data Visualising involves representing processed data using graphs,
charts, diagrams, and other visualizations. Data Summarisation involves calculating various summary
statistics like the mean, median, mode, standard deviation, and variance.
 Modelling
Statistical Modelling of data involves modelling the underlying data distribution and relations in the data
and then making inferences on top of the model. Algorithmic modelling involves using large volumes of
data and optimization techniques to best estimate the distribution and relations of the data, eg Machine
Learning and Deep Learning.

1. Basic building blocks
2. Conditional statements
3. Loop statements
4. String
5. List
6. Dictionary
7. Tuple
8. File handling
9. Function

 Numpy
 Pandas
 Matplotlib
 Seaborn
 Plotly Bokeh
 Sklearn

1. Types of Data- Quantitative, Qualitative
2. Types of Scale- Nominal, Ordinal, Ratio, Interval
3. Measure of Central Tendency
 Mean
 Mode
 Median
4. Measure of Dispersion(Spread
 Range
 Variance
 Standard Deviation
5. Covariance and Correlation
6. Exploratory Data Analysis (EDA)
7. Univariate Analysis
8. Bivariate Analysis
9. Multivariate Analysis

1. Sample & Population- Terminology
2. Sampling Techniques
3. Sampling Errors
4. Continuous & Discrete Random Variables
5. Probability Distribution Functions- Continuous & Discrete
6. Cumulative Distribution Functions
7. Normal Distribution
8. Uniform Distribution
9. Exponential Distribution
10. Bernoulli / Binomial Distribution
11. Poisson Distribution
12. Chi-Square etc…
13. Goal of Statistical Inference
14. Inferences about population parameter from sample statistic using standard normal distribution
15. Sampling Distribution
16. Central Limit Theorem(CLT)
17. Z-score, Confidence level, Confidence Interval, Significance Level

1. What is Hypothesis
2. Null Hypothesis & Alternate Hypothesis
3. Why Hypothesis Testing
4. Types of Hypothesis Tests
5. One sample t-test
6. Paired t-test

7. Z-test
8. Chi-square test
9. One sample proportion test
10. Anova test etc…
11. Types of errors- Type 1 & Type 2

 Identification of variables and data types
 Univariate, bivariate, multivariate analysis
 Variable transformations
 Missing value treatment
 Outlier treatment
 Categorical to Numerical
 Correlation Analysis
 Feature Selection

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What You Will Get

Platforms & Tools

How does it work

How does it work

To assist you in identifying your goals and help achieve them.

What will be assessed?

  • Goals
  • Skill Rating
  • Ongoing assessment to meet your objectives.

Externship refers to virtual employment. Avail this modern trend in employment. Join real companies and become their virtual employee. Solve real problems, get hands-on real-time experience, assess your gaps and then either get the same job or other curated opportunities.

What will you receive?

  • Virtual Employment
  • Portfolio - it can be showcased with your CV

While enactment, you will realize your skill gaps and struggles which then can be explored. You can explore various skills and attend live masterclasses from experts and doubt clearing sessions to meet those gaps.

What will you receive?

  • Online Live masterclasses
  • Live Coaching.
  • Doubt Clearing

Experience comes in the form of job assistance services, mentorship or even getting connected with on-field experts who help you achieve your career goals.

What will you receive?

  • Job Assistance*



Career assistance service to fina a suitable opportunituy - Resume writing, Interview Coaching

Meet Your Instructor

Arpit Yadav
Data Science

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About Education:
• Pursuing Phd in Machine Learning from SVVV, Indore.
• PGP in Artificial Intelligence and Machine Learning from University at Texas at Austin , USA
• M.Tech (VLSI Design) from GHRCE, Nagpur.
• B.E (Electronics and Telecommunication) from JDIET, Yavatmal.

About Achievements:
1. VDGOOD Inspirational Scientist Award in the 7th International Scientist Awards on Engineering, Science and Medicine.
2. Awarded as Excellence in Research (HEI Awards) THE PROGRESS GLOBAL AWARD 2020.
3. Awarded as Best in Innovation, Training and Design Award of “International Association of Educators and Corporate Trainers –IAECT Award 2020”.
4. Awarded as “Best Corporate Trainer Award of “International Association of Educators and Corporate Trainers –IAECT Award 2020”.

I am working as Artificial intelligence and Machine Learning Researcher at tensorBrew, Hyderabad. I am also working as Freelancer Corporate Trainer in Python, Data Science, Machine Learning, Deep Learning, and Artificial Intelligence. I am currently pursuing Ph. D in Machine Learning from SVVV Indore. I have done done PGP in Artificial Intelligence and Machine Learning from Great Lakes, Hyderabad. I have done M.Tech in VLSI Design and B. E in Electronics & Telecommunication Engineering. I am having having 11 Years of Experience in VLSI Research, Machine Learning, Data Science and Artificial Intelligence.
I have done 70+ Certifications in the domain of Data Science, Machine Learning, Deep Learning and Artificial Intelligence. I have conducted many sessions on Data Science , Machine Learning and Artificial Intelligence across India. My other skills include Aptitude Development, Group Discussion, Extempore/Elocution/Debates, Counseling, Motivational Talk, Resume, Writing ,Video Resume Cover Letter ,Expert Talks,Personal Interview/Technical Interview. I am also Giving Training to Competitive Examination/ CRT (Campus Recruitment Training).

Core Skills:

· Tools: Python, VHDL, VLSI Design, Keras, TensorFlow, OpenCV, NLTK.

· Skills: Data Science Using Python, Machine Learning Using Python, Data Analysis, Data Visualization, Deep Learning, Neural Network, Natural Language Processing (NLP), Computer Vision(CV).

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Learner's Project

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