SCOPE OF DATA SCIENCE
Data Science is one of the fastest evolving fields & a Data Scientist’s job is one of the fastest growing and highest paid in tech.
As there is a cut-throat competition in the market, top organizations are turning their minds to data analytics to identify new market opportunities to design their services and products. Surveys show that 75% of top organizations consider data analytics an essential component of business performance. This is where data scientists come in. Data scientists know how to use their skills in math, statistics, programming, and other related subjects to organize large data sets. Then, they apply their knowledge to uncover solutions hidden in the data to take on business challenges and goals. They are thus able to contribute to their organization’s business goals. So, learning data science through effective training can give you a bright future.
“Data Scientist” has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! It is a rewarding career that allows you to solve some of the world’s most interesting problems!
As per Payscale.com, a Data Scientist (IT) with Big Data Analytics skills earns an average salary of Rs 706,750 per year in India.
Skill Venue is delighted to offer classroom program for candidates to help them build a career in the blooming field of Data Science.The program focuses on building foundation skills in business analytics and data science with in depth training of statistical platform & tools.
We will develop candidates to think analytically and solve business problems using data.
Our innovative approach to analytics training by combining deep knowledge and collaborative learning environment will help candidates to develop real skills in analytics.
Candidates taking this course can expect to gain the knowledge of analytical techniques required by organizations to take strategic decisions, and by solving case studies they will know how analytical techniques can be used to get real insights from real data. Candidates will be able to forecast sales, Identify customer segments, identify drivers of sales/profit, perform regression, analyze customer comments and do much more after the course.
In-depth course coverage, hands-on experience of entire data analytics project cycle, and case studies on real world analytics problems, cutting across different domains are the high points that make Skill Venue’s Data Science in R certification course a leap towards a successful analytics career.
WHO SHOULD TAKE THIS COURSE
- IT professionals looking for career or technology change to Data Science & Analytics.
- BPO industry or non technical professionals who are looking for career in Data Science & Analytics.
- Engineering graduates who want to build a career in Data Science & Analytics.
- Non- technical & MBA graduates who are interested in building a career in Data Science.
- Anyone who likes manipulating data & loves getting insights out of data.
- Prerequisite – There are not prerequisite for this course except hard work & dedication.
|Introduction to the Course|
|Introduction to Data ANalysis||00:00:00|
|[Essential Excel Knowledge 1] - Excel Formula Basics & Important Excel Functions|
|0201 Operators Used in Formulas and How to Build Basic Formulas||00:00:00|
|0202 Operator Precedence in Formulas||00:00:00|
|0203 How Built in Excel Functions Make Your Job Easier||00:00:00|
|0204 Inserting Functions into Formulas Effectively||00:00:00|
|0205 Relative Cell References – Using Relative Cell References to Copy Formulas||00:00:00|
|0206 Handling Circular References||00:00:00|
|0207 Using Named Cells and Ranges in the Formulas||00:00:00|
|0208 Using Names for Constants & Formulas & Applying Names to Existing Reference||00:00:00|
|0209 Union and Intersection of Ranges||00:00:00|
|0210 SUM(…) Function – The Most Useful Function in Excel||00:00:00|
|0211 Mathematical Functions: Average(), Max() and Min()||00:00:00|
|0212 Mathematical Functions: INT(), MOD(), ROUND(), RAND(), and RANDBETWEEN()||00:00:00|
|0213 Count Functions in Excel||00:00:00|
|0214 Learn About 3-D Reference in Excel||00:00:00|
|0215 Introducing Array Formulas – How to Enter Arrays into Worksheet Cells||00:00:00|
|0216 Introducing Array Formulas – How to Create Array Formulas with Examples||00:00:00|
|0217 INDEX(…) Function in Excel||00:00:00|
|0218 VLOOKUP(…) Function in Excel||00:00:00|
|[Essential Excel Knowledge 2] - Excel Tables & Using Structured References|
|0301 What is a Table? How a Table Differs from a Normal Range||00:00:00|
|0302 Adding and Deleting Rows or Columns, and Working with the Total Row||00:00:00|
|0303 Removing Duplicate Rows from a Table or a Range||00:00:00|
|0304 Sorting and Filtering a Table and Introducing Custom AutoFilter||00:00:00|
|0305 Using Slicer to Filter a Table||00:00:00|
|0306 Using Formulas in Tables – Summarizing and Referencing Data in a Table||00:00:00|
|0307 Use Structured References in Excel Tables||00:00:00|
|Foundational Concepts of Statistical Data Analysis|
|0401 – Calculating Mean and Median Values||00:00:00|
|0402 – Measuring Maximums, Minimums, and Other Data Characteristics||00:00:00|
|0403 – Analyzing Data Using Variance and Standard Deviation||00:00:00|
|0404 – Introducing Central Limit Theorem||00:00:00|
|0405 – Analyzing a Population Using Data Samples||00:00:00|
|0406 – Identifying and Minimizing Sources of Error||00:00:00|
|0501 – Grouping Data Using Histograms||00:00:00|
|0502 – Identifying Relationships Using XY Scatter Charts||00:00:00|
|0503 – Visualizing Data Using Logarithmic Scales||00:00:00|
|0504 – Adding Trend Lines to Charts||00:00:00|
|0505 – Forecasting Future Results||00:00:00|
|0506 – Calculating Running Averages||00:00:00|
|Testing a Hypothesis|
|0601 – Formulating a Hypothesis||00:00:00|
|0602 – Interpreting the Results of Your Analysis||00:00:00|
|0603 – Considering the Limits of Hypothesis Testing||00:00:00|
|Utilizing Data Distributions|
|0701 – Using the Normal Distribution||00:00:00|
|0702 – Using the Exponential Distribution||00:00:00|
|0704 – Using the Binomial Distribution||00:00:00|
|Measuring Co-variance and Correlation|
|0801 – Visualizing What Co-variance Means||00:00:00|
|0802 – Calculating Co-variance between Two Columns of Data||00:00:00|
|0803 – Calculating Co-variance among Multiple Pairs of Columns||00:00:00|
|0804 – Visualizing What Correlation Means||00:00:00|
|0805 – Calculating Correlation Between Two Columns of Data||00:00:00|
|Case Study: Summarizing Data by Using Histograms|
|0901 – Overview of the section||00:00:00|
|0902 – Stock Return Analysis Using Histograms||00:00:00|
|0903 – Common Shapes of Histograms||00:00:00|
|Case Study: Summarizing Data by Using Descriptive Statistics|
|1001 – Overview of the section||00:00:00|
|1002 – How to Get Descriptive Statistics Using Excel’s Data Analysis ToolPak||00:00:00|
|1003 – What defines a typical value (or centrality) for a data set?||00:00:00|
|1004 – How can I measure how much a data set spreads from its typical value?||00:00:00|
|1005 – What do the mean and standard deviation of a data set tell me about?||00:00:00|
|1006 – How can I use descriptive statistics to compare data sets?||00:00:00|
|1007 – How to easily find the second largest or second smallest number in a data||00:00:00|
|1008 – How can I rank numbers in a data set?||00:00:00|
|1009 – What is the trimmed mean of a data set?||00:00:00|
|1010 – Alternative of Data Analysis ToolPak||00:00:00|
|1011 – Why financial analysts use Geometric mean instead of arithmetic average?||00:00:00|
|Case Study: Estimating Straight-line Relationships|
|1101 – Overview of the section||00:00:00|
|1102 – A brief introduction to dependent and independent variable||00:00:00|
|1103 – The relationship between monthly production & operating costs||00:00:00|
|1104 – Meaning and significance of R-square value||00:00:00|
|1105 – Standard error of regression to measure the accuracy of a relationship||00:00:00|
|1106 – Find intercept, slope and R-square values using Excel’s functions||00:00:00|
|Case Study: Modeling Exponential Growth|
|1201 – Overview of the section||00:00:00|
|1202 – How can I model the growth of a company’s revenue over time?||00:00:00|
|"Case Study: Using Correlations to Summarize Relationships "|
|1301 – Overview of the section||00:00:00|
|1302 – Measuring correlation using Excel’s Data Analysis ToolPak||00:00:00|
|1303 – Filling the matrix||00:00:00|
|1304 – Relation between correlation and R-square value||00:00:00|
|Case Study: Using Moving Averages to Understand Time Series|
|1401 – Overview of the section||00:00:00|
|1402 – How to apply the Moving Average based trend line to show a trend||00:00:00|
|Performing Bayesian Analysis|
|1501 – Introducing Bayesian Analysis||00:00:00|