 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 03 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)

Lecture 2.1What is Analytics & Data Science?

Lecture 2.2Common Terms in Analytics

Lecture 2.3What is data

Lecture 2.4Classification of data

Lecture 2.5Relevance in industry and need of the hour

Lecture 2.6Types of problems and business objectives in various industries

Lecture 2.7How leading companies are harnessing the power of analytics?

Lecture 2.8Critical success drivers

Lecture 2.9Overview of analytics tools & their popularity

Lecture 2.10Analytics Methodology & problem solving framework

Lecture 2.11List of steps in Analytics projects

Lecture 2.12Identify the most appropriate solution design for the given problem statement

Lecture 2.13Project plan for Analytics project & key milestones based on effort estimates

Lecture 2.14Build Resource plan for analytics project

Lecture 2.15Why Python for data science?


Python Core

Lecture 3.1Introduction to python and comparison with other programming language

Lecture 3.2Installation of Anaconda Distribution and other python IDE

Lecture 3.3Python Objects ,Numbers & Booleans, Strings, Container objects, Mutability of v objects

Lecture 3.4Concept of Packages/Libraries – Important packages(Pandas, NumPy, SciPy, v Scikitlearn, Seaborn, Matplotlib)

Lecture 3.5Operators – Arithmetic, Bitwise, Comparison and Assignment Operators

Lecture 3.6Conditions (If else, ifelif)

Lecture 3.7Loops( While,For)

Lecture 3.8Break and Continue Statement.

Lecture 3.9Range Functions


String Objects and Collection

Lecture 4.1String Object Basics

Lecture 4.2String Methods

Lecture 4.3Splitting and joining strings

Lecture 4.4String Format Functions

Lecture 4.5List object Basics

Lecture 4.6List Methods


Tuples, Set, Dictionaries & Functions

Lecture 5.1Tuples, 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

Lecture 6.1OOPS basic concepts

Lecture 6.2Creating classes and objects

Lecture 6.3Working with files

Lecture 6.4Reading and Writing files


Database

Lecture 7.1MongoDB

Lecture 7.2Python SQL


Python Pandas

Lecture 8.1Python pandas – series

Lecture 8.2Python pandas – Dataframe

Lecture 8.3Python pandas – Basic Functionality

Lecture 8.4Python pandas Reindexing

Lecture 8.5Python pandas – Iteration

Lecture 8.6Python pandas – Sorting

Lecture 8.7Python pandas – Working with Text data

Lecture 8.8Python pandas – options and customization

Lecture 8.9Python pandas – Indexing and selecting data

Lecture 8.10Python pandas – Date Functionality

Lecture 8.11Python pandas – Categorical Data

Lecture 8.12Python pandas – Visualization


Data Manipulation

Lecture 9.1Cleansing – Munging Cleansing Data with Python

Lecture 9.2Filling missing values using lambda function and concept of Skewness.

Lecture 9.3Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)


Data Analysis : Visualization Using Python

Lecture 10.1Introduction exploratory data analysis

Lecture 10.2Descriptive statistics, Frequency Tables and summarization

Lecture 10.3Univariate Analysis (Distribution of data & Graphical Analysis)

Lecture 10.4Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)

Lecture 10.5Creating Graphs Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)

Lecture 10.6Important Packages for Exploratory Analysis( NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)


Introduction To Statistics

Lecture 11.1Descriptive Statistics

Lecture 11.2Sample vs Population Statistics

Lecture 11.3Random variables

Lecture 11.4Probability distribution functions

Lecture 11.5Expected value

Lecture 11.6Normal distribution

Lecture 11.7Gaussian distribution

Lecture 11.8Zscore

Lecture 11.9Central limit theorem

Lecture 11.10Spread and Dispersion

Lecture 11.11Hypothesis testing

Lecture 11.12Zstats vs Tstats

Lecture 11.13Type 1 & Type 2 error

Lecture 11.14Confidence Interval

Lecture 11.15ANOVA Test

Lecture 11.16Chi Square Test

Lecture 11.17Ttest 1Tail 2Tail Test

Lecture 11.18Correlation and Covariance


Introduction To Predictive Modeling

Lecture 12.1Concept of model in analytics and how it is used?

Lecture 12.2Common terminology used in Analytics & Modeling process

Lecture 12.3Popular Modeling algorithms

Lecture 12.4Types of Business problems – Mapping of Techniques

Lecture 12.5Different Phases of Predictive Modeling


Data Exploration For Modeling

Lecture 13.1Need for structured exploratory data

Lecture 13.2EDA framework for exploring the data and identifying any problems with the data by the help of pair plot.

Lecture 13.3Identify missing data

Lecture 13.4Identify outliers data

Lecture 13.5Visualize the data trends and patterns


Data Preparation

Lecture 14.1Merging

Lecture 14.2Normalizing the data

Lecture 14.3Feature Engineering

Lecture 14.4Feature Selection

Lecture 14.5Feature scaling using Standard Scaler

Lecture 14.6Label encoding

Lecture 14.7Need of Data preparation

Lecture 14.8Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values Dummy creation – Variable Reduction

Lecture 14.9Variable Reduction Techniques – Factor & PCA Analysis
