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What is Technical Analysis

Technical Analysis is a method of analyzing securities such as stock, commodities, etc. in order to forecast the direction of pricing by studying past data such as the price and volume. It focuses on how stick prices are moving and how powerful these moves are. Technical analysis is based solely on the data generated by the market and by the actions of people in the market. Data are never revised later. Analysts do not make any guesses on the value of the data. It is based on the premise that people will act in similar ways when faced with similar conditions.

But to be more pragmatic, it is a tool used to make investment decisions. It helps assess risk and reward. And it can assist investors in allocating their resources among stocks, sectors, and asset classes.

The above picture depicts the factors upon which Technical Analysis is dependent upon.

What Technical Analysis is not?

Technical analysis is not predicting the future of an endorsement or criticism of any company. There is an element of prediction and judgment as it attempts to find the probability of future action of a company’s stock but not the company itself that is under scrutiny.

Also, this analysis does not give an absolute prediction about the price movement but helps the investors and traders anticipate what could likely happen and accordingly take an investment decision.

What is a chart?

Technical analysis has been a bit of a mistake as it’s not that technical. Though there are complex mathematical tied to it but at its core, it is the method of determining if a stock or market as a whole is going up or going down. What we need to do in order to identify these trends is simply looking at a chart. Now let’s understand charts and how they help in Technical Analysis.

A chart is a tool both investors and traders use to help them determine whether to buy or sell the stock-bond commodity or currency. As mentioned bar charts summarize all the trading for any given time period such as a day or a week when all those summaries are plotted together trends emerge and patterns form – all revealing where a stock is right now and how it got there. After all, knowing a stock is trading at a price of 50 is not of much help but knowing it was at 45 last month and 40 the month before gives us a good idea that it has been a bullish trend.

Charts are where perception meets reality. For instance, a stock may look cheap according to an analyst’s calculations based on projected future earnings, but if there were no demand for the stock it is simply not going to go up.

Some analysts look at a chart and simply draw an arrow on the actual data plot if the arrow is pointing up they know the trend is up and vice versa.

On the charts, we look at what is happening right now and how it came to be. From there we make an educated guess about the future, but the goal is not to predict where prices will be in a year. The real goal is to determine what we do about it right now. If we decide to buy based on a chart, we will already know what has to happen to prove us wrong and that helps us limit losses.

Understanding Each Part Of Chart


Price action tells us what the supply and demand equilibrium is at any given point in time..


Volume is very useful In determining whether a stock is cooling back in correction or changing direction. And another important use is is in the identification of both the final panic and initial surges as investor moods change from one extreme to another.


Momentum indicators quantify what the naked eye can tell us about the price action. Momentum In the market causes trends to stay in effect until halted by outside factors.


The way price action is used on charts in twofold; either the stock in question is moving or is not. The former creates a trend either higher or lower the latter creates resting zones and further shapes of these resting zones give us clues as to when the next trend will be up or down. It is the structure of these ups downs and flats that is analyzed.


The sentiment is the summation of all market expectations. It ranges from fear and hopelessness to indifference to greed and complacency. At the bottom of a bear market, the expectations of the participants are almost unanimous for a lower prices and more financial losses.

What are the support and resistance?


At some price level, a falling stock price will stabilize. Enough investors will perceive it to be good value and demand shares; while others will perceive the price to be too low for them to want to sell any more of their holdings.


Why use Technical Analysis?

The next logical question that comes to one’s mind is what does technical analysis do? The answer to this is that it has the ability to recognize when a stock has reached support or resistance level or a shift in perception takes place can help investors take investment decisions i.e. to use:

Buy low, sell high approach or

Buy high, sell higher approach, or

Whether to buy the stock or not

The ability to apply these aspects of the chart will reveal the market to investors when it is safe to buy a stock or not. Technical analysis is the only investment decision discipline that tells you when you are wrong to minimize losses.

Practice effective risk analysis and management. Practice effective risk analysis and management. Design and deliver creative solutions to your clients’ investment needs.

Practice effective risk analysis and management. Practice effective risk analysis and management. Design and deliver creative solutions to your clients’ investment needs.

Goals of Technical Analysis

Seeing where the stock is currently trading and figuring out how it got there

This can be done by using charting tools such as the

Stock trends

Support levels

Resistance levels

We also try to find out a pattern or a trend to help.

Determining the power of a trend

This can be determined by looking at important technical concepts such as trading volume and momentum.

Making comparisons of the stock to the market, peer companies, and itself

For this we look at the relative performances and moving averages (average prices over a defined period of time, usually 50-200 days)

Criteria for Investment using technical analysis tools

In looking for a stock, the following are the key technical analysis tools that should be met, not necessarily all bot most.

Trends and trendlines

Trends can be classified in three ways: Up, Down or Range bound. In an uptrend, a stock rallies often with intermediate periods of consolidation or movement against the trend. In a downtrend, a stock declines often with intermediate periods of consolidation or movement against the trend. In range-bound, there is no apparent direction to the price movement on the stock chart and there will be little or no rate of price change.

These trends can be measured using trendlines. All we want is stocks that are in rising trends.

Support and Resistance

This basically tells us what price levels are likely to bring put buyers or the sellers. Here we want to see that if the current price has either just moved through resistance or one that is far from the next resistance level.

Moving Averages

They help in determining if the trend is turning, also it shows if the existing trend is progressing in an orderly manner or no. here we are looking at prices to be above-selected averages but not too far above them.

Relative Performance

This divides the price of a stock by a relative market index or industry group. Here the theory is to find out if the ratio is going up the stock is outperforming the market and is thus a strong candidate for further gains and vice versa. Here we are looking for stocks whose relative performances are increasing.


The number of shares traded and when (either when prices rise or when they fall). We basically analyze if buying is spreading to other investors and for urgency for all to buy when prices start to rise.


We want to know if the days when the stock rises outnumber those when it falls. If losing days are more and frequent then we can say the trend is weakening.


It is to find out if everybody is thinking the same thing? Is it time to go the other way? Here we want to know what everyone is thinking about the same thing.

 A career in Technical Analysis

Any person who enjoys working with numbers and is keen about statistics and capital markets may enjoy being a technical analyst.

Technical Analyst Job Description

Technical analysts study the trends and patterns of the stocks to make predictions about its future performance. They find out this sophisticated information to find out the best time and price at which to sell stocks. These professionals are often employed by finance and investment agencies, financial institutions, and brokerage houses.

Technical Analyst Pre-requisites

A bachelor’s degree in commerce major, like economics or finance, is required for a career in technical analysis. Some firms may require employees to have Master of Business Administration or master’s degrees in finance.



Analytical skills

Communication skills

Good in maths and statistics

Knowledge of computer software programs

You're reading Technical Analysis – Beginners Guide To Technical Analysis

Beginner’s Guide For Data Analysis Using Sql

This article was published as a part of the Data Science Blogathon

Overview Important Definitions

The most significant part of the database is its tables, which contain all of the data. Normally, data would be divided among several tables rather than being saved all in one location (so designing the data structure properly is very important). The majority of this script would deal with table manipulation. Aside from tables, there are a few more extremely helpful concepts/features that we will not discuss:

table creation

inserting/updating data in the database

functions – takes a value as an input and returns a value that has been manipulated (for example function that remove white spaces)

#Improts import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) import sqlite3 import matplotlib.pyplot as plt # Load data from database.sqlite database = 'database.sqlite' We’ll start by connecting to the database and seeing what tables we have.

The query’s basic structure is straightforward: After the SELECT, you specify what you wish to see; * denotes all possible columns. Following the FROM, you select the table. After the WHERE, you add the conditions for the data you wish to use from the table(s).

The section’s structure and order of content, with spaces, new lines, capital letters, and indentation to make the code easier to understand.

conn = sqlite3.connect(database) tables = pd.read_sql("""SELECT * FROM sqlite_master WHERE type='table';""", conn) tables List of countries

This is the most basic query. The only must part of a query is the SELECT and the FROM (assuming you want to pull from a table)

countries = pd.read_sql("""SELECT * FROM Country;""", conn) countries List of leagues and their country

When joining tables, you must do the following:

Choose the type of join you want to utilize. The following are the most common:

INNER JOIN – only maintain records in both tables that match the criterion (after the ON), and records from both tables that don’t match won’t appear in the output.

LEFT JOIN – all values from the first (left) table are combined with the matching rows from the right table. NULL values would be assigned to the columns from the right table that doesn’t have a corresponding value in the left table.

Specify the common value that will be used to link the tables together (the id of the country in that case).

Ensure that at least one of the values in the table is a key. It’s the chúng tôi in our case. Because there can be more than one league in the same nation, the id is not unique.

leagues = pd.read_sql("""SELECT * FROM League JOIN Country ON chúng tôi = League.country_id;""", conn) leagues List of teams

ORDER BY defines the sorting of the output – ascending or descending (DESC)

LIMIT limits the number of rows in the output – after the sorting

teams = pd.read_sql("""SELECT * FROM Team ORDER BY team_long_name LIMIT 10;""", conn) teams

We’ll just show the columns that interest us in this example, so instead of *, we’ll use the actual names.

The names of several of the cells are the same (, We’ll use AS to rename them.

This query, as you can see, includes a lot more joins. The reason for this is that the database is designed in a star structure, with one table (Match) containing all of the “performance” and metrics, but only keys and IDs, and other tables including all of the descriptive information (Country, League, Team)

It’s important to note that the Team is joined twice. This is a hard one because, despite the fact that we are using the same table name, we are bringing two separate copies (and rename them using AS). The reason for this is that we need to bring data for two separate values (home team API id and away team API id), and joining them to the same database would imply that they are equal.

It’s also worth noting that the Team tables are linked together using a left join. The reason for this is that I’ve decided to keep the matches in the output, even if one of the teams isn’t on the Team table.

ORDER comes before LIMIT and after WHERE and determines the output order.

detailed_matches = pd.read_sql("""SELECT chúng tôi chúng tôi AS country_name, chúng tôi AS league_name, season, stage, date, HT.team_long_name AS home_team, AT.team_long_name AS away_team, home_team_goal, away_team_goal FROM Match JOIN Country on chúng tôi = Match.country_id JOIN League on chúng tôi = Match.league_id LEFT JOIN Team AS HT on HT.team_api_id = Match.home_team_api_id LEFT JOIN Team AS AT on AT.team_api_id = Match.away_team_api_id WHERE country_name = 'Spain' ORDER by date LIMIT 10;""", conn) detailed_matches Let’s do some basic analytics

Here we are starting to look at the data at a more aggregated level. Instead of looking at the raw data, we will start to group it to the different levels we want to examine. In this example, process the previous query, remove the match and date information, and look at it at the country league season level.

The functionality we will use for that is GROUP BY, which comes between the WHERE and ORDER

Once you chose what level you want to analyze, we can decide the select statement into two:

Dimensions are the values we’re describing, and they’re the same ones we’ll group by later.

Metrics must be grouped together using functions. sum(), count(), count(distinct), avg(), min(), and max() are some of the most common functions.

It’s critical to use the same dimensions in both the select and the GROUP BY functions. Otherwise, the output could be incorrect.

HAVING is another feature that can be used after grouping. This adds another layer of data filtering, this time using the table’s output after grouping. It’s frequently used to clean the output.

leages_by_season = pd.read_sql("""SELECT chúng tôi AS country_name, chúng tôi AS league_name, season, count(distinct stage) AS number_of_stages, count(distinct HT.team_long_name) AS number_of_teams, avg(home_team_goal) AS avg_home_team_scors, avg(away_team_goal) AS avg_away_team_goals, avg(home_team_goal-away_team_goal) AS avg_goal_dif, avg(home_team_goal+away_team_goal) AS avg_goals, sum(home_team_goal+away_team_goal) AS total_goals FROM Match JOIN Country on chúng tôi = Match.country_id JOIN League on chúng tôi = Match.league_id LEFT JOIN Team AS HT on HT.team_api_id = Match.home_team_api_id LEFT JOIN Team AS AT on AT.team_api_id = Match.away_team_api_id WHERE country_name in ('Spain', 'Germany', 'France', 'Italy', 'England') GROUP BY,, season ORDER BY,, season DESC ;""", conn) leages_by_season df = pd.DataFrame(index=np.sort(leages_by_season['season'].unique()), columns=leages_by_season['country_name'].unique()) df.loc[:,'Germany'] = list(leages_by_season.loc[leages_by_season['country_name']=='Germany','avg_goals']) df.loc[:,'Spain'] = list(leages_by_season.loc[leages_by_season['country_name']=='Spain','avg_goals']) df.loc[:,'France'] = list(leages_by_season.loc[leages_by_season['country_name']=='France','avg_goals']) df.loc[:,'Italy'] = list(leages_by_season.loc[leages_by_season['country_name']=='Italy','avg_goals']) df.loc[:,'England'] = list(leages_by_season.loc[leages_by_season['country_name']=='England','avg_goals']) df.plot(figsize=(12,5),title='Average Goals per Game Over Time') df = pd.DataFrame(index=np.sort(leages_by_season['season'].unique()), columns=leages_by_season['country_name'].unique()) df.loc[:,'Germany'] = list(leages_by_season.loc[leages_by_season['country_name']=='Germany','avg_goal_dif']) df.loc[:,'Spain'] = list(leages_by_season.loc[leages_by_season['country_name']=='Spain','avg_goal_dif']) df.loc[:,'France'] = list(leages_by_season.loc[leages_by_season['country_name']=='France','avg_goal_dif']) df.loc[:,'Italy'] = list(leages_by_season.loc[leages_by_season['country_name']=='Italy','avg_goal_dif']) df.loc[:,'England'] = list(leages_by_season.loc[leages_by_season['country_name']=='England','avg_goal_dif']) df.plot(figsize=(12,5),title='Average Goals Difference Home vs Out') Query Run Order

Now that we are familiar with most of the functionalities being used in a query, it is very important to understand the order that code runs.

First, order of how we write it (reminder):









Define which tables will be used and how they will be connected (FROM + JOIN).

Only the rows that apply to the conditions should be kept (WHERE)

Sort the information by the required level (if need) (BY GROUP)

Select the data you wish to include in the new table. It can contain only raw data (if there is no grouping), or a combination of dimensions (from the grouping), as well as metrics. You’ve decided to show the following from the table.

Order the new table’s output (ORDER BY)

Add extra filtering conditions to the newly generated table (HAVING)

Limit the number of rows – this would reduce the number of rows, as well as the need for filtering (LIMIT)

Sub Queries and Functions

Use a subquery as a solution. The attributes database would need to be grouped to a different key-player level only (without season). Of course, we would need to decide first how we would want to combine all the attributes to a single row. use AVG, also one can decide on maximum, latest season and etc. Once both tables have the same keys, we can join them together (think of the subquery like any other table, only temporal), knowing that we won’t have duplicated rows after the join.

You can also see two examples of how to use functions here:

– A conditional function is an important tool for data manipulation. While the IF statement is widely used in other languages, SQLite does not support it, hence CASE + WHEN + ELSE is used instead. As you can see, the query would return varied results depending on the data input.

– ROUND – straightforward. Every SQL language comes with a lot of useful functions by default.

players_height = pd.read_sql("""SELECT CASE WHEN ROUND(height)<165 then 165 ELSE ROUND(height) END AS calc_height, COUNT(height) AS distribution, (avg(PA_Grouped.avg_overall_rating)) AS avg_overall_rating, (avg(PA_Grouped.avg_potential)) AS avg_potential, AVG(weight) AS avg_weight FROM PLAYER LEFT JOIN (SELECT Player_Attributes.player_api_id, avg(Player_Attributes.overall_rating) AS avg_overall_rating, avg(Player_Attributes.potential) AS avg_potential FROM Player_Attributes GROUP BY Player_Attributes.player_api_id) AS PA_Grouped ON PLAYER.player_api_id = PA_Grouped.player_api_id GROUP BY calc_height ORDER BY calc_height ;""", conn) players_height players_height.plot(figsize=(12,5)) EndNote About the Author

Connect with me on Github

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.


Ib Pitchbook – Liquidity Analysis

IB Pitchbook – Liquidity Analysis

Download our free Investment Banking Pitchbook Template

Written by

CFI Team

Published January 19, 2023

Updated July 7, 2023

Liquidity Analysis

An analysis of a company’s liquidity is important because it gives us insight into its capacity to pursue an M&A transaction. We need to identify trends in a company’s liquidity position, what their needs are over time, and the implications to the company’s liquidity if a transaction is to be pursued. When performing a liquidity analysis, the main points to consider are the company’s cash flow profile, capital expenditures, debt, and future funding requirements.

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Cash Flow Profile

The company’s ability to generate cash from operation is the main focus of liquidity analysis. As an investment banker, you must consider any significant trends or shifts in the company’s variable and fixed costs. How is the performance of the company’s margins over time? What inventory costing do they use (LIFO, FIFO, weighted average cost)? What depreciation methods do they use (straight line, declining balance)? How does their financial accounting differ from their tax accounting, and its implications on tax deferral? Are there any major gains and impairments that should be considered?

All of these questions contribute to the overall sustainability of the company’s operations and its overall capacity to pursue a transaction. How much of the transaction can be funded internally? How much additional capital must be raised? What type of capital can be raised and what is the strategic rationale for raising one form of capital over another?

Capital Expenditures

The company’s CapEx schedule is very important when pitching a transaction opportunity because it is the main opportunity cost to consider against a transaction opportunity. For example, a company can invest in its own capital that will replicate the benefit of a transaction. Furthermore, the amount of capital available to a company may already be committed to specific capital requirements, it is an investment banker’s job to calculate the requirement and frame a strategic recommendation around these existing commitments.

When we consider a company’s capital expenditures, it is important to distinguish between growth and maintenance CapEx. While it is critical for a company to continually invest in maintenance CapEx to replace any depreciation, the amount of growth CapEx could be the amount that a company might forgo to pursue a transaction. If M&A is a regular course of business (i.e. AutoCanada, Premium Brands), the growth CapEx may already be factoring in transactions on an ongoing basis.


The company’s leverage is probably the most important element to consider when pitching a transaction. If we think about accretion/dilution, due diligence lets us choose an appropriate range of stock vs cash breakdown in terms of limits on the amount of leverage a company and its creditors may be comfortable with. Furthermore, taking on too much debt to fund a transaction may cause the company to incur interest beyond what it can pay down. Also, if a company faces any major debt maturities in the near future, it may opt to conserve its dry powder in anticipation of the debt coming due.

A company may include items that behave like debt, and we must consider any operating or financial leases by the company, as well as any pension obligations the company is committed to paying out. Additionally, we must think about how much room the company has in short-term credit facilities, and the company’s capital allocation priorities before pitching a transaction.

If the target company is also leveraged, we must take into consideration the fact that the target’s enterprise value includes the value of its debt. Therefore, it is important to consider the change in a company’s leverage ratios pro forma the transaction.

Additional Resources

Exploratory Data Analysis (Eda)

Exploratory Data Analysis (EDA) is the process of summarization of a dataset by analyzing it. It is used to investigate a dataset and lay down its characteristics. EDA is a fundamental process in many Data Science or Analysis tasks.

Different types of Exploratory Data Analysis

There are broadly two categories of EDA

Univariate Exploratory Data Analysis – In Univariate Data Analysis we use one variable or feature to determine the characteristics of the dataset. We derive the relationships and distribution of data concerning only one feature or variable. In this category, we have the liberty to use either the raw data or follow a graphical approach.

In the Univariate raw data approach or Non-Graphical, we determine the distribution of data based on one variable and study a sample from the population. Also, we may include outlier removal which is a part of this process.

Let’s look into some of the non-graphical approaches.

The measure of Central tendency − Central tendency tried to summarize a whole population or dataset with the help of a single value that represents the central value.

The three measures are the mean, the median, and the mode.

Mean − It is the average of all the observations. i.e., the sum of all observations divided by the number of observations.

Median − It is the middle value of the observations or distribution after arranging them in ascending or descending order.

Mode − It is the most frequently occurring observation.

Variance − It indicates the spread of the data about the middle or Mean value. It helps us gather info regarding observations concerning central tendencies like mean. It is calculated as the mean of the square of all observations.

Skewness − It is the measure of the symmetry of the observations. The distribution can either be left-skewed or right skewed forming a long tail in either case.

Kurtosis − It measures how much-tailed a particular distribution is concerning a normal distribution. Medium kurtosis is known as mesokurtic and low kurtosis is known as platykurtic.

In the Univariate graphical approach, we may use any graphing library to generate graphs like histograms, boxplots, quantile-quantile plots, violin plots, etc. for visualization. Data Scientists often use visualization to discover anomalies and patterns. The graphical method is a more subjective approach to EDA. These are some of the graphical tools to perform univariate analysis.

Histograms − They represent an actual count of a particular range of values. It shows the frequency of data in the form of rectangles’ which is also known as bar graph representation and can be either vertical or horizontal.

Box plots − Also known as box and whisker plots. They use lines and boxes to show the distribution of data from one or more than one groups. A central line indicates the median value. The extended line captures the rest of the data. They are useful in the way that they can be used to compare groups of data and compare symmetry.

Q-Q plots − To determine if two datasets come from the same or different distribution, a Q- Q plot is used.

Multivariate Exploratory Data Analysis − In Multivariate analysis we use more than one variable to show the relationships and visualizations. It is used to show the interaction between different fields.

Multivariate Non-Graphical (raw data) − Techniques like tabulation of more than two variables. ANOVA test can also play a significant role.

Multivariate Graphical − In visualization analysis for multivariate statistics, the below plots can be used.

Scatterplot − It is used to display the relationship between two variables by plotting the data as dots. Additionally, color coding can be intelligently used to show groups within the two features based on a third feature.

Heatmap − In this visualization technique the values are represented with colors with a legend showing color for different levels of the value. It is a 2d graph.

Bubble plot − In this graph circles are used to show different values. The radius of the circle on the chart is proportional to the value of the data point.

Programming Language tools used in EDA

Both R and Python languages can be used to perform EDA. These languages are very powerful for EDA and provide some of the best tools out of the box. Let us see some of the utilities of these languages.

R language − R language was developed by Ross Ihaka and Robert Gentleman. R is a modular programming language with function support. It can be integrated with procedures written in C/C++. R has some of the very powerful tools for data analysis and graphing often used by researchers, data scientists, analysts, etc.

Python language − Python is a high-level language. It is readable and uses indents to separate blocks of code. It supports several features like structured and object-oriented programming, functions, and paradigms. It is a very simple language and uses fewer lines of code to perform a particular task than other programming languages. Python has many tools for exploratory data analysis and visualization like pandas,matplotlib, seaborn,dask, etc.


Technical Specifications For The 2023 Iphone Se

Apple on April 15, 2023 announced the new low-cost iPhone SE model that packs in plenty of worthwhile specifications despite the lower price tag compared to other iPhones.

Table of contents

Continue reading for the full iPhone SE tech specs.

iPhone SE (2023) tech specs

For historical purposes, and to make it obvious what the 2023 iPhone SE offers, we’ve put together a quick breakdown of the technical specifications for the handset.

In the box

iPhone SE with iOS 13

EarPods with Lightning connector

Lightning to USB cable

USB power adapter


Color options




Storage capacity

64 GB

128 GB

256 GB

Available space is less and varies due to many factors. Depending on the iPhone SE model and settings, a standard configuration uses approximately 11GB to 14GB of space, which includes the iOS operating system and Apple’s preinstalled apps. Preinstalled apps use about four gigabytes of storage space, but you can delete these apps (and restore them at any time).

Apple chips

Apple A13 Bionic system-on-a-chip:

64-bit architecture

Third-generation Apple Neural Engine for machine learning and AI

Apple M12 coprocessor for motion tracking

Water and dust resistance

Splash, water and dust resistant

Rater IP67 (maximum depth of 1 metro up to 30 minutes)

Keep in mind that splash, water and dust resistance are not permanent conditions: resistance might decrease as a result of normal wear. Liquid damage is not covered under warranty.


Retina HD widescreen LCD display with IPS technology

4.7-inch diagonally


1334-by-750-pixel resolution at 326 ppi

1400:1 contrast ratio (typical)

True Tone display

Wide color display (P3)

Supports Dolby Vision and HDR10 video content

Haptic Touch

625 nits max brightness (typical)

Fingerprint-resistant oleophobic coating

Display Zoom



Front camera:

Sensor features:

7 megapixels

ƒ/2.2 aperture

Wide color capture for photos and Live Photos

Photography features:

Portrait Lighting with six effects: Natural, Studio, Contour, Stage, Stage Mono and High-Key Mono

Auto HDR for photos

Auto image stabilization

Burst mode

Timer mode

Exposure control

Retina flash

Photo geotagging

HEIF and JPEG capture formats

Video recording:

1080p HD video recording at 30 frames per second

Cinematic video stabilization (1080p and 720p)

QuickTake video

Playback zoom

Video geotagging

HEVC and H.264 video capture formats

Rear camera:

Sensor features:

12 megapixels

Wide camera

ƒ/1.8 aperture

Six‑element lens

Sapphire crystal lens cover

Optical image stabilization for photos and video

Photography features:

Portrait Lighting with six effects: Natural, Studio, Contour, Stage, Stage Mono and High-Key Mono

Next-generation Smart HDR for photos

Digital zoom 5x

Advanced red-eye correction

Auto image stabilization

LED True Tone flash with Slow Sync

Burst mode

Panorama 63MP

Photo geotagging

Wide color capture for photos and Live Photos

Image capture formats: HEIF and JPEG

Video recording:

4K video recording at 24, 30 or 60 frames per second

1080p HD video recording at 30 or 60 frames per second

720p HD video recording at 30 frames per second

Optical image stabilization for video

Digital zoom 3x

LED True Tone flash

QuickTake video

1080p slow-mo recording at 120 or 240 frames per second

Time‑lapse video with stabilization

Cinematic video stabilization (4K, 1080p and 720p)

Continuous autofocus video

Take 8MP still photos while recording 4K video

Playback zoom

Video geotagging

Video formats: HEVC and H.264

Stereo recording

Speakers and microphones

Stereo audio via the speakers at the bottom and in the earpiece

Microphones at the bottom and in the earpiece


Touch ID fingerprint sensor


Three-axis gyroscope


Proximity sensor

Ambient light sensor


Touch ID:

Fingerprint sensor built into the Home button

Unlock iPhone SE

Secure personal data within apps

Make purchases from iTunes Store, App Store and Apple Books

External buttons and connectors

Home/Touch ID button

Ring/Silent switch

Lightning connector for power, data and expansion

Side button (on/off, sleep/wake)

Volume up and down buttons

SIM card

Dual SIM technology with nano-SIM and eSIM support

iPhone SE is incompatible with existing micro-SIM cards

Power, data and expansion



Rechargeable lithium ion battery

Wireless charging (works with Qi chargers)

Charging via USB to computer system or power adapter

2023 iPhone SE lasts about the same as iPhone 8:

Video playback: 13 hours

Video playback (streamed): 8 hours

Audio playback: 40 hours

Fast charge: 50% large in 30 minutes with 18W+ adapter (sold separately)

See chúng tôi and chúng tôi for more information.

Cellular and wireless

Model A2275:

FDD‑LTE (bands 1, 2, 3, 4, 5, 7, 8, 12, 13, 14, 17, 18, 19, 20, 25, 26, 29, 30, 66 and 71)

TD‑LTE (bands 34, 38, 39, 40, 41, 42, 46 and 48)

CDMA EV‑DO Rev. A (800 and 1900 MHz)

UMTS/HSPA+/DC‑HSDPA (850, 900, 1700/2100, 1900 and 2100 MHz)

GSM/EDGE (850, 900, 1800 and 1900 MHz)

All models:

Gigabit-class LTE with 2×2 MIMO and LAA4

802.11ax Wi‑Fi 6 with 2×2 MIMO

Bluetooth 5.0

NFC with reader mode

Express Cards with power reserve

Location services

Built-in GPS/GNSS

Digital compass


Cellular network

iBeacon microlocation

Video calling

FaceTime video calling over Wi-Fi and cellular

Audio calling

FaceTime Audio

Voice over LTE (VoLTE)

Wi-Fi calling

Rating for hearing aids

M3, T4

Size and weight

Width: 2.65 inches (67.3 mm)

Height: 5.45 inches (138.4 mm)

Depth: 0.29 inch (7.3 mm)

Weight: 5.22 ounces (148 grams)

Environmental requirements

Operating ambient temperature: 32° to 95° F (0° to 35° C)

Nonoperating temperature: −4° to 113° F (−20° to 45° C)

Relative humidity: 5% to 95% noncondensing

Operating altitude: tested up to 10,000 feet (3000 m)

Read the iPhone SE Environmental Report for more detailed information.

Made with better materials:

100% recycled rare earth elements in the Taptic Engine

100% recycled tin in the solder of the main logic board

Enclosure made with recyclable, low-carbon aluminum

35% or more recycled plastic in multiple components

Energy efficient:

Meets US Department of Energy requirements for battery charger systems

Smarter chemistry:

Arsenic-free display glass

Mercury-free display

BFR, PVC and beryllium-free

Green manufacturing:

Final assembly supplier sites do not generate any waste sent to landfill

All final assembly suppliers are transitioning to 100% renewable energy for Apple production

Responsible packaging:

100% of virgin wood fiber comes from responsibly managed forests

Recyclable, majority-fiber packaging

According to Apple, the Taptic Engine in the 2023 iPhone SE represents about 34 percent of the total rare earth elements used in the product.


Operating system:

iOS 13 (some features may not be available for all countries)

Apple Pay:

Pay with your iPhone SE using Touch ID within apps and on the web

Send and receive money in Messages with Apple Pay Cash


Use your voice to send messages, set reminders and more

Get proactive suggestions

Use hands-free with “Hey Siri”

Listen and identify songs with Shazam

Use your voice to run shortcuts from your favorite apps

Preinstalled Apple apps:

App Store

Apple Store











Find My



iTunes Store

iTunes U






















Voice Memos





Are you liking the hardware specifications of the 2023 iPhone SE?

Can Ai Improve Business While Technical Singularity Unfolds?

Depending on whether you prefer utopian or dystopian science fiction, the singularity will either be absolutely fabulous or downright horrifying. Mathemetician and science-fiction writer Vernor Vinge is thought to have coined the term “the singularity.” Vinge’s view in this 1993 essay is decidedly dystopian: “Within thirty years, we will have the technological means to create superhuman intelligence. Shortly after, the human era will be ended.”

While Vinge’s timeline may be a little off (we assure you this was written by a human, not a robot), the ideas he espoused have taken root and caused consternation for great minds such as Stephen Hawking, Ray Kurzweil, and Elon Musk, among many others. Meanwhile, AI marvels such as IBM’s Watson and Google’s AlphaGo have performed a variety of amazing tasks. Watson famously beat a human in Jeopardy! in 2011, and AlphaGo defeated a human in the ancient and complex game of Go.

One of the first new machine-learning products from Google, according to Re/Code, is a jobs API designed to help companies looking to hire hundreds of workers at a time. Re/Code reports that Career Builder, Dice, and FedEx are planning to use the new service.

“The costs of processing, memory, bandwidth, sensors, and storage continue to fall exponentially. Cloud computing will make all these resources available on demand across the world. Digital data will become only more pervasive, letting us run experiments, test theories, and learn at an ever-greater scale. And the billions of humans around the world are growing increasingly connected; they’re not only tapping into the world’s knowledge (much of which is available for free) but also expanding and remixing it. This means that the global population of innovators, entrepreneurs, and geeks is growing quickly and, with it, the potential for breakthroughs.”

Where AI excels, according to McAfee and Brynjolfsson, is in jobs that involve pattern recognition. These can be tasks such as recognizing street signs, parsing human speech, identifying credit fraud, and modeling how materials will behave under different conditions, they write, predicting “[J]obs that involve matching patterns, in particular, from customer service to medical diagnosis, will increasingly be performed by machines.”

Back To The Real World

While it’s fun to ponder how AI and the singularity might transform our future, it’s far more meaningful for most of us working in IT to consider what’s happening right now as the AI rubber meets the corporate road. Infosys attempts to provide some answers in its January 2023 research report, “Amplifying Human Potential: Towards Purposeful Artificial Intelligence,” which is based on a survey of 1,600 business and IT decision makers worldwide conducted in November 2023 by Vanson Bourne.

According to the report, AI is perceived as a long-term strategic priority for innovation, with 76% of survey respondents citing AI as fundamental to the success of their organization’s strategy, and 64% believing their organization’s future growth is dependent on large-scale AI adoption.

The IT department is the leading adopter of AI in the enterprise, according to 69% of respondents, followed by operations (34%), business development (33%), marketing (29%) and commercial, sales and customer services (28%).

How are organizations preparing for AI deployment and use? Here’s how it breaks down (multiple responses were allowed):

·  Investing in supporting IT infrastructure (60% of respondents)

·  Developing knowledge/skills (53%)

·  Using external support to assist with planning (46%)

·  Building AI into company ethos (43%)

·  Using external support for knowledge gathering (40%)

·  Gathering feedback from customers (32%)

·  Assessing customer / industry approach (25%)

The Infosys report offers a fairly rosy outlook for employees. In 80% of cases where companies are replacing roles with AI, the study finds, organizations are redeploying or retraining staff to retain them in the business. Furthermore, 53% are specifically investing in skills development. Organizations with fewer AI-related skills are more likely to re-deploy workers impacted by AI adoption, whereas those with more AI-related skills are more likely to re-train employees, according to the study. The leading industries that plan to retain and retrain their workers are: fast-moving consumer goods (94% of respondents); aerospace and automotive (87%); energy, oil and gas (80%); and pharmaceutical and life sciences (78%).

Respondents say their organizations have deployed, or are planning to deploy, AI in the following technology areas (multiple responses were allowed):

·  Big data automation (65% of respondents)

·  Predictive/prescriptive analytics (54%)

·  Machine learning (51%)

·  Expert systems – software that leverages databases and repositories to assist decision-making (44%)

·  Deep learning neural networks (31%)

On average, the companies surveyed by Vanson Bourne for the Infosys report have invested $6.7 million in AI in the last year, and have been actively using AI for an average of two years.

Respondents in pharmaceuticals/life sciences are most likely to say their organizations have fully and successfully deployed AI technology (40%) with respondents in the public sector most likely to say their organizations have no plans to use it (27%).

The Infosys report also explores which skills organizations will seek from future generations in the workforce. More than half of respondents (58%) cite active learning and 53% cite complex problem-solving and as key skills. Other important skills are critical thinking (46% of respondents) creativity (46%), and logical reasoning (43%). The most important academic subjects respondents see as focus areas for future generations are computer sciences (72%), business and management (47%), and mathematics (45%).

When asked about which skills their current employees offer to implement and use AI, it’s clear most organizations have room for improvement. Roughly half of all respondents say their current workforce has the following necessary skills (multiple responses were allowed):

·  Development skills (58% of respondents)

·  Security skills (58%)

·  Implementation skills (57%)

·  Training skills (47%)

·  Customer-facing skills (37%)

·  No AI skills (10%)

In their report “Why AI Is The Future Of Growth,” Accenture analysts Mark Purdy and Paul Daugherty write: “With AI as the new factor of production, it can drive growth in at least three important ways. First, it can create a new virtual workforce—what we call ‘intelligent automation.’ Second, AI can complement and enhance the skills and ability of existing workforces and physical capital. Third, like other previous technologies, AI can drive innovations in the economy. Over time, this becomes a catalyst for broad structural transformation as economies using AI not only do things differently, they will also do different things.”

McAfee and Brynjolfsson also suggest positive changes through AI, as long as we humans can avoid standing in our own way. “In times of rapid change, when the world is even less predictable than usual, people and organizations need to be given greater freedom to experiment and innovate,” they write in their Foreign Affairs article. “In other words, when one aspect of the capitalist dynamic of creative destruction is speeding up—in this case, the substitution of digital technologies for cognitive work—the right response is to encourage the other elements of the system to also move faster. Everything from individual tasks to entire industries is being disrupted, so it’s foolish to try to lock in place select elements of the existing order. Yet often, the temptation to try to preserve the status quo has proved irresistible.”

Perhaps if we could be more like AI, we wouldn’t have to overcome what the Infosys report identifies as the top five barriers to adoption: employee fear of change; lack of in-house skills to implement and manage AI; lack of knowledge about where AI can exist; concerns about handing over control; and cultural acceptance.

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