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Mlminds course enables you to master the concepts of Data Science.

Basics of Business Analytics

Business analytics is the practice of iterative, methodical exploration of an organization’s data, with an emphasis on statistical analysis. Business analytics is used by companies committed to data-driven decision-making. It is about using your data to derive information, insights, knowledge, and recommendations. Businesses use business analytics to improve effectiveness and efficiency of their solutions.

In this module, I will talk about how analytics has progressed from simple descriptive analytics to being predictive and prescriptive. I will also talk about multiple examples to understand this better and discuss various industry use cases. I will also introduce multiple components of big data analysis including data mining, machine learning, web mining, natural language processing, social network analysis, and visualization in this module. Lastly, I will provide some tips for learners of data science to succeed in learning and applying data science successfully for their projects.

Types of analytics
  • Descriptive analytics, predictive analytics, prescriptive analysis
Introduction to components of big data analytics
  • Brief Introduction about Components of Big Data Analysis
  • Introduction to Hadoop and Big Data
  • Infrastructure
  • Introduction to Data Mining
  • Introduction to Machine Learning
  • Introduction to Nature Language Processing
  • Introduction to Information Retrieval
  • Introduction to Web Mining
  • Introduction to Social Network Analytics
  • Introduction to IOT
  • Introduction to Visualization
Practical Data Science

1.Lots of open positions available in the industry
2.High paying jobs
3.DataScience makes the product better.
4.Reduces of lot of human work and can create impact on the society

Python for data science

Python is an interpreted high-level programming language for general-purpose programming. Created by Guido van Rossum and first released in 1991, Python has a design philosophy that emphasizes code readability, notably using significant whitespace. Python is open source, has awesome community support, is easy to learn, good for quick scripting as well as coding for actual deployments, good for web coding too.

In this module, I will start with basics of the Python language. We will do both theory as well as hands-on exercises intermixed. I will use Jupyter notebooks while doing hands-on. I will also discuss in detail topics like control flow, input output, data structures, functions, regular expressions and object orientation in Python. Closer to data science, I will discuss about popular Python libraries like NumPy, Pandas, SciPy, Matplotlib, Scikit-Learn and NLTK.

Basics of the Python language
  • Why Python
  • Python Installation
  • Python 2.7 Vs 3.x
  • Introduction to Essential Python Libraries
  • Introduction to iPython and Jupyter Notebooks
  • Python Language Basics- Indentation, Comments, Function Calls, Variables and Argument Passing
  • Python Language Basics-Types, Duck-Typing, Import
  • Python Language Basics-Binary operators, Comparisons, Mutable
  • Python Language Basics-Standard Data types in Python
  • Python Language Basics-Command Line Arguments
Python Language Basics-Control Flow
  • Loops: for, while
  • Conditional Execution
Input/Output in Python
  • Input, output, Eval, Print
  • repr, str, zfill
  • File IO
  • JSON I/O with Python Dictionary
  • JSON I/O with Generic objects
  • JSON I/O Serialization and Deserialization
  • JSON I/O File
  • Introduction to Pickle
  • cPickle
  • Pickle and Multi-Processing
Python Data Structures and Sequences
  • Tuples
  • List
  • Sorting, Searching, Slicing
  • Built-In Functions-Enumerate, Sort, Zip, Reversed
  • Dictionary
  • Sets
  • Lists, Sets and Dict Comprehensions
Functions
  • Introduction to Functions and Variable Length Argument
  • Namespace, Scope, Local Funtions, Local vs Global Variables
  • Returning multiple vales, Pass by Reference
  • Functions are objects
  • Recursive functions, Anonymous(Lambda) Functions
  • Currying, Generators
  • Itertools Module
  • Errors and Exception Handling
  • Introduction to Functions and Variable Length Argument
  • Namespace, Scope, Local Funtions, Local vs Global Variables
  • Returning multiple vales, Pass by Reference
  • Functions are objects
  • Recursive functions, Anonymous(Lambda) Functions
  • Currying, Generators
  • Itertools Module
  • Errors and Exception Handling
Object Orientation in Python
  • Python Modules and Packages
  • object oriented Nature of Python
  • Class Inheritance, overriding, overloading, Data Hiding
Regular expressions in Python
  • Searching for patterns, matching groups
  • Regular expression flags
  • split, findall, finditer
  • Repetition syntax
  • Character sets, Exclusion, Character Ranges, Escape Codes
  • Substitution
  • Greedy vs non-greedy matching
  • Backreferences and anchors
  • Capturing parts of pattern match
  • split and zero-width assertions
  • Look-arounds
NumPy
  • Introduction to Numpy and ndarrays
  • Datatypes of ndarrays
  • Arithmetic operations, Indexing, Slicing
  • Boolean and fancy indexing
  • Basic ndarray operations
  • Array-oriented programming with arrays
  • Conditional, Statistical and Boolean operation
  • Sorting and set operation
  • File IO with NumPy
  • Linear Algebra for Numpy
  • Reshaping, Concatenating and Splitting Arrays
  • Broadcasting
Pandas
  • Series Data Structures
  • DataFrame
  • Index objects
  • Reindexing
  • Dropping entries from an axis
  • Indexing, Selection and Filtering
  • Arithmetic and Data Alignment
  • Operations between DataFrame and Series
  • Function Application and Mapping
  • Sorting and Ranking
  • Axis indexes with duplicate labels
  • Computing Descriptive Statistics
  • pct_change(), Correlation and Covariance, Unique values, Value counts and membership
Visualization
  • Introduction to Matpotlib
  • Colours, Markers and line styles
  • Customization of Matplotlib
  • Plotting with Pandas
  • Barplots, Histograms plots, Density Plots
  • Introduction to Seaborn, Style Management
  • Controlling figure aesthetics
  • Colour Palettes
  • Plotting univariate Distribution
  • Plotting bivariate Distribution
  • Visualizing pairwise relationship in pairplots
  • Plotting with Categorical Data
  • Visualizing Linear Relationships
  • Plotting on Data-aware grids
  • Other Python Visualization tools
SciPy
  • Linear Algebra in SciPy
  • Sparse Matrices in SciPy
  • Constants, Cluster and FFT Packages
  • Integration using SciPy
  • Interpolation in SciPy
  • SciPy I/O, SciPy ndimage
  • Optimization and root finding
  • SciPy.Stats
Scikit learn
  • Introduction to SciKit Learn and Machine Learning
  • Sample Dataset in SciKit Learn
  • Train Test using SciKit Learn
  • Classification IRIS using Decision Trees
  • Holdout Validation, K-fold cross Validation
  • Cross Validation using SciKit Learn
  • K-means Clustering in SciKit Learn
Basic Text Mining using Python
  • Introduction to Nature Language Processing tool kit
  • Tokenization, Lower casing and removing stop words, Lemmatization, Stemming
  • ngrams, Sentence tokenization, Part of speech tagging
  • Chunking, Named Entity Recognition
  • Introduction to WordNet, and word sense disambiguation
Mini Projects
  • Word ladders game Read more
  • Data Analysis and Prediction using the Loan Prediction Dataset Read more

Manish Gupta

Instructor

He is also an Adjunct Faculty at the International Institute of Information Technology, Hyderabad and a visiting faculty at the Indian School of Business, Hyderabad. He received his Masters in Computer Science from IIT Bombay in 2007 and his Ph.D. from the University of Illinois at Urbana-Champaign in 2013.

FEE

INR  3500

Length:  25+ Hours

Validity:  1 year (365 days)

Course Features

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Projects

Assignments/ Quizes

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Disclaimer

Please note that the videos are not downloadable. Sharing your access or trying to sell or distribute videos is a legally punishable offence. Earlier we caught some people doing this and they were punished legally and a huge penalty was imposed on them.

Rohini kumar. M

I have read a lot of DS books before joining this course, I had difficulty in understanding the intuition behind some algorithms..after watching Manish sir’s teaching of those complex algorithms I have got a clear understanding of those algorithms thanks to Ravi sir for bringing this course to students.

Jyothi

The support from the team was very quick, the questions are answered within 24 hrs through mails/phone calls…This course helped in cracking many interviews in DS field..most of the questions asked during interviews were taught in this course.-

Rahul

I have not seen a course which teaches both python and R required for ds.Mathematical explanations given for algorithms were simply awesome.Thanks to Manish sir for making concepts clear.

Ram

The course content is the vast and best which is more than required for a fresher to start their career in ds field. Thanks to Ravindra babu Ravula sir and Manish sir for providing such a large content. Manish sir explained most of the complex concepts with some history behind those concepts to cutting edge use cases of those concepts in industry.

Aman

Manish sir covered each and every concept from scratch. I have attended many interviews all the questions asked in the interview were covered in these course in the simplest way possible.

Priya Basu

The best part about this course was customer support and No prerequisite. I feel anyone who is interested in a data science course can take this course. Manish sir’s way of teaching complex and advanced concepts will just simple blew you away.

Anjali Thakur

I have taken many courses for ds/ml.. but this course like heaven to me. They covered complete end to end concepts in ds from web scrapping to building optimal ml models. My queries regarding concepts were solved within 24 hours. Thanks to team for making my concepts much more clear.

Romitha

I am very happy with the course content and customer support provided by MLminds.Course videos connected all the dots.Thanks to Ravindrababu ravula sir and Manish sir for providing such a great lectures.

Seema Sen

After finishing the course content now I can confidently say that I can give first cut solution to most of the ml problems.Mathematical explanations given for ml algorithms were simply awesome.A huge thanks to Manish sir..

Gaurav

I am addicted to Manish sir’s way of teaching difficult concepts in the simplest way possible. Thanks to the team for resolving all the queries.

Dilip

In my personal opinion those who are looking to change their careers to the data science field. Mlminds is a one-stop solution. Completely impressed by the Manish sir’s teaching.

Aditya

I finished the course last month and now I could able to crack most of the data science interviews very easily.Thanks to Manish sir for combining your industry experience and knowledge and delivering it.

Aakash

The best part about the course the Manish sir has explained every integrity details of the ml algorithms with code and the customer support provided by the team was super.

Arbaz

This course will definitely change the way you think about new deep learning algorithms that are evolving nowadays. Thanks to Manish sir for explaining at a very deep level of each and every algorithm.

Shivam

Thanks to Manish sir for making my foundations strong in maths. Now I’m confident to learn any new ml algorithm through research papers. Thanks to the team of MLminds for patiently explaining even my tiny doubts regarding the videos.

Raj

All the old school maths I have learned during my schooling and college were been just a dots in my mind. Thanks a lot to Manish sir for connecting all those dots.

Everything will be taught from scratch. No prerequisite.

Data science content by Microsoft experts.

Master data science course 1000+ students registered

MLMinds

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