Master Degree in Data Science

Instructor: skillcircle
Lectures: 95
Duration: 120 hours

Categories: live

Data field is evolving each day and new opportunities are coming everyday. Keeping this in mind we have designed a Master Program in Data science under the exceptional mentorship of leading data scientists from the data fraternity.
For 6 months you will be learning, practicing data science with Capstone projects and hands on practical exposure.

After the end of cohort, you will be working as data scientist as Full time candidature or as a Trainee with some leading data science company.

Eligibility

Applicants should have 50% or above in Xth, XIIth and Bachelor’s degree.
The program is open for candidates in their final semester and recent graduates with 0-3 years of experience.
The Data Science Course is ideal for candidates with a graduation in a quantitative discipline like engineering, mathematics, commerce, sciences, statistics, economics, BBA,BCOM,MBA,BMS,BBS

Introduction to Data Science with Python ( Induction) 0/15
- Lecture 2.1
  
  What is Analytics & Data Science?
- Lecture 2.2
  
  Common Terms in Analytics
- Lecture 2.3
  
  What is data
- Lecture 2.4
  
  Classification of data
- Lecture 2.5
  
  Relevance in industry and need of the hour
- Lecture 2.6
  
  Types of problems and business objectives in various industries
- Lecture 2.7
  
  How leading companies are harnessing the power of analytics?
- Lecture 2.8
  
  Critical success drivers
- Lecture 2.9
  
  Overview of analytics tools & their popularity
- Lecture 2.10
  
  Analytics Methodology & problem solving framework
- Lecture 2.11
  
  List of steps in Analytics projects
- Lecture 2.12
  
  Identify the most appropriate solution design for the given problem statement
- Lecture 2.13
  
  Project plan for Analytics project & key milestones based on effort estimates
- Lecture 2.14
  
  Build Resource plan for analytics project
- Lecture 2.15
  
  Why Python for data science?
Python Core 0/9
- Lecture 3.1
  
  Introduction to python and comparison with other programming language
- Lecture 3.2
  
  Installation of Anaconda Distribution and other python IDE
- Lecture 3.3
  
  Python Objects ,Numbers & Booleans, Strings, Container objects, Mutability of v objects
- Lecture 3.4
  
  Concept of Packages/Libraries – Important packages(Pandas, NumPy, SciPy, v Scikit-learn, Seaborn, Matplotlib)
- Lecture 3.5
  
  Operators – Arithmetic, Bitwise, Comparison and Assignment Operators
- Lecture 3.6
  
  Conditions (If else, if-elif)
- Lecture 3.7
  
  Loops( While,For)
- Lecture 3.8
  
  Break and Continue Statement.
- Lecture 3.9
  
  Range Functions
String Objects and Collection 0/6
- Lecture 4.1
  
  String Object Basics
- Lecture 4.2
  
  String Methods
- Lecture 4.3
  
  Splitting and joining strings
- Lecture 4.4
  
  String Format Functions
- Lecture 4.5
  
  List object Basics
- Lecture 4.6
  
  List Methods
Tuples, Set, Dictionaries & Functions 0/1
- Lecture 5.1
  
  Tuples, Sets, Dictionary object basics, Dictionary, Object methods, Dictionary v view objects, Functions basics, Iterators, Generator functions, Lambda Functions, Map, Reduce & filter functions
OOPS concepts & Working with Files 0/4
- Lecture 6.1
  
  OOPS basic concepts
- Lecture 6.2
  
  Creating classes and objects
- Lecture 6.3
  
  Working with files
- Lecture 6.4
  
  Reading and Writing files
Database 0/2
- Lecture 7.1
  
  MongoDB
- Lecture 7.2
  
  Python SQL
Python Pandas 0/12
- Lecture 8.1
  
  Python pandas – series
- Lecture 8.2
  
  Python pandas – Dataframe
- Lecture 8.3
  
  Python pandas – Basic Functionality
- Lecture 8.4
  
  Python pandas- Reindexing
- Lecture 8.5
  
  Python pandas – Iteration
- Lecture 8.6
  
  Python pandas – Sorting
- Lecture 8.7
  
  Python pandas – Working with Text data
- Lecture 8.8
  
  Python pandas – options and customization
- Lecture 8.9
  
  Python pandas – Indexing and selecting data
- Lecture 8.10
  
  Python pandas – Date Functionality
- Lecture 8.11
  
  Python pandas – Categorical Data
- Lecture 8.12
  
  Python pandas – Visualization
Data Manipulation 0/3
- Lecture 9.1
  
  Cleansing – Munging Cleansing Data with Python
- Lecture 9.2
  
  Filling missing values using lambda function and concept of Skewness.
- Lecture 9.3
  
  Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
Data Analysis : Visualization Using Python 0/6
- Lecture 10.1
  
  Introduction exploratory data analysis
- Lecture 10.2
  
  Descriptive statistics, Frequency Tables and summarization
- Lecture 10.3
  
  Univariate Analysis (Distribution of data & Graphical Analysis)
- Lecture 10.4
  
  Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
- Lecture 10.5
  
  Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
- Lecture 10.6
  
  Important Packages for Exploratory Analysis( NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)
Introduction To Statistics 0/18
- Lecture 11.1
  
  Descriptive Statistics
- Lecture 11.2
  
  Sample vs Population Statistics
- Lecture 11.3
  
  Random variables
- Lecture 11.4
  
  Probability distribution functions
- Lecture 11.5
  
  Expected value
- Lecture 11.6
  
  Normal distribution
- Lecture 11.7
  
  Gaussian distribution
- Lecture 11.8
  
  Z-score
- Lecture 11.9
  
  Central limit theorem
- Lecture 11.10
  
  Spread and Dispersion
- Lecture 11.11
  
  Hypothesis testing
- Lecture 11.12
  
  Z-stats vs T-stats
- Lecture 11.13
  
  Type 1 & Type 2 error
- Lecture 11.14
  
  Confidence Interval
- Lecture 11.15
  
  ANOVA Test
- Lecture 11.16
  
  Chi Square Test
- Lecture 11.17
  
  T-test 1-Tail 2-Tail Test
- Lecture 11.18
  
  Correlation and Co-variance
Introduction To Predictive Modeling 0/5
- Lecture 12.1
  
  Concept of model in analytics and how it is used?
- Lecture 12.2
  
  Common terminology used in Analytics & Modeling process
- Lecture 12.3
  
  Popular Modeling algorithms
- Lecture 12.4
  
  Types of Business problems – Mapping of Techniques
- Lecture 12.5
  
  Different Phases of Predictive Modeling
Data Exploration For Modeling 0/5
- Lecture 13.1
  
  Need for structured exploratory data
- Lecture 13.2
  
  EDA framework for exploring the data and identifying any problems with the data by the help of pair plot.
- Lecture 13.3
  
  Identify missing data
- Lecture 13.4
  
  Identify outliers data
- Lecture 13.5
  
  Visualize the data trends and patterns
Data Preparation 0/9
- Lecture 14.1
  
  Merging
- Lecture 14.2
  
  Normalizing the data
- Lecture 14.3
  
  Feature Engineering
- Lecture 14.4
  
  Feature Selection
- Lecture 14.5
  
  Feature scaling using Standard Scaler
- Lecture 14.6
  
  Label encoding
- Lecture 14.7
  
  Need of Data preparation
- Lecture 14.8
  
  Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
- Lecture 14.9
  
  Variable Reduction Techniques – Factor & PCA Analysis

skillcircle

Shivam is an entrepreneur and marketing consultant for different startups.

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Master Degree in Data Science

Eligibility

Introduction to Data Science with Python ( Induction) 0/15

Python Core 0/9

String Objects and Collection 0/6

Tuples, Set, Dictionaries & Functions 0/1

OOPS concepts & Working with Files 0/4

Database 0/2

Python Pandas 0/12

Data Manipulation 0/3

Data Analysis : Visualization Using Python 0/6

Introduction To Statistics 0/18

Introduction To Predictive Modeling 0/5

Data Exploration For Modeling 0/5

Data Preparation 0/9

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