Data Analytics - Key to Success
Learn Data Analysis with Python and transform your career into a success story
Add Value to Organization
Add tremendous value to the growth of your organization by mastering the art of extracting meaningful insights from data
Learn Data Analytics from Experts
Master the mathematics, statistics, and Python programming skills required to be successful Data Analytics under the supervision of experts
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Data Analysis with Python

Experienced and qualified faculty

Classroom sessions conducted by IIT, IIM alumni with 40 years of industry experience

Focus on concepts with practicals

Focus on Concepts

Emphasis on concepts of Data Analysis with Python, statistics, linear algebra, numpy, pandas, and seaborn

Instructor led classroom sessions

50+ hour of instructor-led classroom sessions. Additional 50+ hours for projects and assignments

Virtual classroom sessions

Option of attending the session from home using video conference. Join live classroom and participate in discussion

Know about the upcoming batches

We have designed the program for beginners, who do not have any prior knowledge in Data Analysis.  The concepts of linear algebra, statistics and python programming are explained in an easy way to help you learn the Data Analysis with Python

  • 31 August, 2019
  • Time - 10 am - 1 pm
  • Sector 48, Gurgaon / Kalkaji New Delhi
  • Duration - 2 month - 100+ hours
  • Projects - 2
  • Total Sessions - 16 on weekends
  • Training Fee - Rs 18000
  • Enrollment Fee - Rs 5000
  • 21 September, 2019
  • Time - 2 pm - 5 pm
  • Kalkaji, New Delhi
  • Duration - 2 month - 100+ hours
  • Projects - 2
  • Total Sessions - 16 on weekends
  • Training Fee - Rs 18000
  • Enrollment Fee - Rs 5000
  • 12 October, 2019
  • Time - 10 am - 1 pm
  • Kalkaji, New Delhi
  • Duration - 2 months - 100+ hours
  • Projects - 2
  • Total Sessions - 16 on weekends
  • Training Fee - Rs 18000
  • Enrollment Fee - Rs 5000

Why Data Analysis with Python?

Data Science has gained popularity in the recent past. Data science deals in converting a huge amount of raw data to provide meaningful insights and strategy. The data is gathered from many sources, then it is structured and studied to gain insights. The process of drawing the key insights from structured and unstructured data is known as “Data Science”. The professionals working on gaining such insights and presenting them to key stakeholders are known as “Data Analysts” and “Data Scientists“. Today, data science finds its place in retail, finance, e-commerce, healthcare, and IT services industries.

Today data has become the necessity of all the organization. McKinsey estimated that that big data initiative in the US healthcare system “could account for $300 billion to $450 billion in reduced healthcare spending or 12 to 17 percent of the $2.6 trillion baselines in US healthcare costs”

A Data Scientist and Data Analysts add tremendous value to their organization. Due to this they have become modern-day superheroes, Some of the advantages of data science in business are:

  • Risk Mitigation and Fraud: Data scientists can find data anomalies using statistical techniques. They can use predictive analytical techniques to create alerts when unusual data is recognized.
  • Delivering relevant products: Using data analysis techniques, organizations can find when and where their products sell best. They can plan their inventories, production plans, and product placements to meet their customer requirements more effectively
  • Personalized customer experience: Data analyst can help an organization understand its customer better and device the techniques to deliver the best customer experience

What you will learn?

Key concepts of Python programming, Python libraries, statistical analysis techniques and linear algebra. More over you will get an opportunity to work on 2 projects that will help not only in consolidating the concepts learnt but also the experience on how to solve real life data analytics problems.

  • Python Programming concepts
  • Use of Python libraries: Numpy, Pandas, Matplotlib, Seaborn
  • Concepts of statistical analysis using probability distributions and other statistical techniques
  • Data cleaning and preparation
  • Joining, combining, reshaping and pivoting data
  • Data aggregation and group operations, quantile and bucket analysis
  • Data plotting and data visualization using matplotlib and seaborn

Course Curriculum

Data Analysis with Python covers the topics related to Python Programming, Numpy, Pandas, Matplotlib, Seaborn, Data Analysis using Python, Statistical tools and techniques, and Linear Algebra. The detailed training in these areas will help you solve any data analysis problems

Python Programming

  1. Installation of Python 3.0 on a local machine and setting up a programming environment
  2. Your first program
  3.  IPython Basic
    1. Running IPython Shell
    2. Running Jupyter Notebook
    3. Tab Completion
    4. Introspection
    5. %run Command
    6. Magic Commands
  4. Python Programming Basics
  5. Writing comments in Python
  6.  Understanding Data Types
  7.  Working with strings
  8.  How to format text in Python 3
  9.  String Functions in Python 3
  10.  Convert Data Types
  11.  Using Variables
  12.  String Formatters
  13.  Math in Python and Built in Functions for Numbers
  14.  Boolean Logic
  15.  Lists in Python 3
  16.  Use List Methods
  17.  List Comprehensions
  18.  Tuples and Dictionaries
  19.  How to import and write modules
  20.  Conditional statements
  21.  For, and While loops and Break, Continue and Pass Statements
  22. Functions and Lambda Functions
  23. Error and Exception Handling

Matplotlib

  1. Figures and Subplots
  2. Colors, Markers, and Line Styles
  3. Ticks, Labels, and Legends
  4. Annotation and Drawing on a subplot

Seaborn

  1. Line, Bar, Histogram, Density, Scatter Plots

Numpy

  1. Numpy ndarray
  2. Creating ndarrays
  3. Arithmetic with ndarrays
  4. Basic Indexing and Slicing
  5. Boolean Indexing
  6. Transposing Arrays
  7. Universal Functions
  8. Array Oriented Programming with Arrays
  9. File Input and Output
  10. Pseudorandom Number Generation

Pandas

  1. Pandas Data Structures
  2. Series
  3. DataFrame
  4. Reindexing
  5. Dropping Entries from an axis
  6. Indexing, Selection and Filtering
  7. Integer Index
  8. Arithmetic and Data Alignment
  9. Function Application and Mapping
  10. Sorting and Ranking
  11. Reading and Writing Data

Data Analysis

  1. Handling missing  data
  2. Filtering and filling missing data
  3. Data transformation
  4. Removing duplicates
  5. Discretization and Binning
  6. Detecting and filtering outliers
  7. Permutation and Random Sampling
  8. String Manipulation
  9. String Object Methods
  10. Vectorized String Functions
  11. Hierarchical Indexing
  12. Reordering sorting levels
  13. Summary Statistics by Level
  14. Combining and Merging datasets
  15. Reshaping and Pivoting
  16. Reshaping with hierarchical indexing
  17. GroupBy Mechanics
  18.  Iterating over groups
  19. Grouping with Dicts and Series
  20. Grouping with functions
  21. Data Aggregation
  22. Suppressing the Group Keys
  23. Quantile and Bucket analysis
  24. Pivot tables and cross-tabulation

Statistics

  1. Graphically Displaying Single Variable
  2. Mean, Median
  3. Range, Variance, Standard Deviation
  4. Scatterplot
  5. Scatterplot Matrix
  6. Covariance and Correlation
  7. Probability
  8. Joint Probability
  9. Independent events
  10. Conditional probability
  11. Bayes’ Theorem
  12. Prior, Likelihood and Posterior
  13. Discrete Random Variable
  14. Probability Distribution of
  15. Discrete Random Variable
  16. Binomial Distribution
  17. Continuous Random Variables
  18. Probability Distribution Function
  19. Uniform Distribution
  20. Normal Distribution
  21. Beta Distribution
  22. Point Estimation
  23. Interval Estimation
  24. Expectation Theory
  25. Hypothesis Testing – one sided, two sided

Linear Algebra

  1. Systems of Linear Equations
  2. Row Reduction and Echelon Forms
  3. Existence and Uniqueness
  4. Vectors and Matrix Equations
  5. Vector Matrix Products
  6. Linear Independence
  7. Matrix Multiplication
  8. Transpose of Matrix
  9. Inverse of Matrix
  10. Vector Space, Null Space, Row Space
  11. Eigenvalues and Eigenvectors
  12. Orthogonality

Projects

  1. Random Walks, Simulating many random Walks
  2. Determine Brand Persona for Cycle Sharing Scheme using statiscal tools and techniques

Key Learning Areas

The learning areas include hands on expertise in Python programming and its libraries, statistical analysis of data, and concepts of linear algebra. The program focuses on building expertise in  data analysis concepts. Even if you do not have any expertise in Python programming and data analysis, you will learn faster from our classes.

Python
Learn concepts of Python programming language
Statistics
Concepts of probability, probability distributions
Linear Algebra
Working with linear equations, vectors, and matrices

Join the training program

Learn the latest technology and be part of a revolution. 

Training Program Fee Rs 18000 + GST

Registration Fee Rs 5000

Watch our live training sessions to know
more about our training pedagogy

Our Office Locations

Head Office - Gurgaon
Samatrix Consulting Pvt Ltd
311 Vipul Trade Centre
Sector 48, Sohna Road
Gurgaon - 122018
India
New Delhi
NDIIT
105. Nehru Pl Road,
Block 1, Kalkaji
New Delhi – India
110019
Bengaluru
166, Second Floor,
5th Main Road, MC Layout,
Opp BDA Complex,
Vijayanagar,
Bengaluru
Karnataka – India
Bijnor
Samatrix Consulting Pvt Ltd
1st Floor,
Near Shakthi Chowk,
Civil Lines
Bijnor – Uttar Pradesh – India
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Contact Info

  • 311, Vipul Trade Centre, Sector 48, Sohna Road, Gurgaon , Haryana 122018, India
  • +91 8130534589
  • [email protected]