Teleman Certified Data Science Professional (TCDSP)
Teleman Certified Data Science Professional (TCDSP)
Data Science Course: Instructor – Led (Classroom) – Weekend Program
Data Science Course is best suited for fresher or experienced aspirants who are looking to start or switch their career in Data Science or Data Analytics field.
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Although our capabilities to store and process data have been increasing exponentially since the 1960s, suddenly many organizations realize that survival is not possible without exploiting available data intelligently. Out of the blue, ‘‘Big Data’’ has become a topic in board-level discussions. The abundance of data will change many jobs across all industries. Moreover, also scientific research is becoming more data-driven.
Just like computer science emerged as a new discipline from mathematics when computers became abundantly available, we now see the birth of data science as a new discipline driven by the torrents of data available today.
Mike Driscoll writes in “The Three Sexy Skills of Data Geeks”: “…with the Age of Data upon us, those who can model, munge, and visually communicate data—call us statisticians or data geeks—are a hot commodity.”
January 2009 Hal Varian, Google’s Chief Economist, tells the McKinsey Quarterly: “I keep saying the sexy job in the next ten years will be statisticians. People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s? The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades… Because now we really do have essentially free and ubiquitous data.”
Level I – To understand data,
Level II- To get insights by analysing data,
Level III- to become a proficient data scientist.
The flow of the course has been designed to get deeper understanding of the tools ( R and Python) used by data scientist, by using them as tools to deep dive into analytics and visualization instead of teaching them as individual products.The course also introduces participants to Neural Networks and will prepare them to pursue for Advance learning at a later stage.
Duration: 3 Months (100 hrs) | Assignments (100 Hrs. Approx)
Qualification: Must be appearing or passed any graduation with mathematics knowledge.
Other course requirements : Must have Laptop, should bring to class daily.
Mode of delivery: Classroom/slides/videos
Course includes practical exposure to Tools for data Analysis, Case studies, Projects etc.( Case studies related to data interpretation and final project.)
Sectors which are recruiting Data science experts are
IT & services
And many others
The course is designed in a way to address most of the company interview questions and hence will help the candidates crack the interviews more easily and to become an adept data scientist. It can lead the participants to following jobs/designations :
Data consultant, data analyst, data scientist, consumer insights manager, Data science manager , Business Analyst, Business Intelligence Analyst , Data Architect, Data Engineer, Data Mining engineer , Business Intelligence manager depending upon company structure and education level of the participant.
TELEMAN'S TRAINING METHODOLOGY
At Teleman you learn every topic from basics, because of which you not only develop better understanding of subject but also you can out perform in Interviews. It is observed that we tend to forget part of previously taught topics after moving ahead with newer ones, at Teleman we follow a unique training methodology to help you develop long term expertise on topics.
a) About Big data: What is Big Data , Analytics and its importance, Applications of analytics, Difference between Statistics and Analytics, Difference between Data Engineering and Data Science.
Learnings: Here we will introduce you to the world of Analytics and how organisations are using it to prosper in today’s competitive market.
b) Exploring data : Mean, median, mode | Q1, Q3, IQR | Data, data types | Variables | Central tendency | Data distribution | Case Study.
c) Introduction to Database and ETL : SQL Introduction, Data processing through ETL
Learnings: Here you will learn how to develop understanding on your data and how can you work with your data to find out meaningful insights from it.
Basics of Statistics: Probability | Bayes theorem | Binomial Distribution | Hypothesis testing | Case Study
Learnings: An introduction to the basic concepts of statistics will not only help you to understand the story of behind our data but is also key for data analysis
A) Introduction to R: R Base Software | Understanding CRAN | R Studio The IDE Vectors | Basic and Advanced Operations | Operators and Types | R Functions and loops Working with R data frames | Data Visualization in R(Utility, Limitations using ggplot.
Learning: R is one of the most popular and powerful tools for data analysis. It is free and open source. You can use multiple free packages and even create your own packages.
B) Data Analysis in R: Data preparation and processing (using dplyr) | Outlier Treatment | Transforming Variables | Handling Missing Values | Binning and modifying data with Base R | Hypothesis Testing in R | Practice assignment
Learning: Data visualization techniques are extremely important to build a story and provide meaningful insights to the top level management. Here, you will learn how to create beautiful visualizations with R
Data cleaning is important before we process our data set. We will teach you how to use R to clean and manipulate the data
Python Programming: What is Python? | Installing Anaconda | Understanding the Spyder Integrated Development Environment (IDE) | Lists, Tuples, Dictionaries, Variables | Introduction to Numpy Arrays | Creating ndarrays | Indexing | Data Processing using Arrays | File Input and Output | Getting Started with Pandas | In class hands-on
Learnings: This module will help you to learn the powerful capabilities of Python that will make it easy for you to work with data.
Machine Learning and Analytics: Identifying the problem statement | Qualitative and Quantitative Research | Analysis of Variance and Covariance using R | Correlation using R
Learnings: Understanding the problem statement and data clearly is very important before we proceed with our analysis.
Regression Techniques ( Simple Linear, Multiple Linear, Logistic Regression, Case Study) | Methods of model validation ( Model Assumptions and odds ratio | Adjusted R squared | Cross-validation and average error | Over-fitting | Bootstrap algorithm.) | Clustering (K-Means Clustering | Hierarchical Clustering | Case Study) | Decision trees and Random Forests (What are Decision trees? | Entropy | Gini’s Index | Basics of decision tree algorithms | Usefulness of random forest | Case Study) |
Learnings: In this module you will learn about the various techniques of data analysis, how to build different models and also how to validate your model. Validation of your model helps to identify the best fit model with fewer errors in the model. Decision trees and Random forests are one of the most popular classification and prediction method to help in decision making.
Predictive Modelling and Forecasting techniques: (Linear Regression Model, Logistic Regression Model, Adjusted R, P-value | SVM | Time series analysis (Need for time-series forecasting, Trends and seasonality factor, Exponential smoothing, ARIMA model, Case study) | Analysis with R and Python | Problem solving) | Introduction to sentiment analytics |
Learning: In this module you will understand the concepts of predictive modelling and forecasting. Wouldn’t it be amazing if you can forecast or predict the future?. Through this module, you will learn various machine learning algorithms to solve business problems. Industry related problems will not only enhance your understanding but will also give you the impetus to crack interviews and get recruited by big MNC’s
Data visualization tools and techniques (Tableau/PowerBI)
Learnings: Here, we will teach you to use Industrial tools and techniques of data visualisation.
In class hands-on and case studies
Neural Networks: Perceptron | MLP | Back Propagation | Using Tensor Flow
Learnings: Introduction to neural networks and the way forward!